Systems and methods for predicting degradation of a battery for use in an electric vehicle

ABSTRACT

A system for predicting degradation of a battery for use in an electric vehicle is presented. The system includes a computing device communicatively connected to at least a pack monitor unit, wherein the at least a pack monitor unit is configured to detect a battery pack datum of a plurality of battery modules incorporated in a battery pack. The computing device is further configured to receive the battery pack datum as a function of the at least a pack monitor unit, generate, as a function of the battery pack datum, a battery pack model associated with the battery pack of the electric vehicle, and determine a battery degradation prediction as a function of the battery pack datum and the battery pack model.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of Non-provisional application Ser.No. 17/524,265 filed on Nov. 11, 2021 and entitled “SYSTEMS AND METHODSFOR PREDICTING DEGRADATION OF A BATTERY FOR USE IN AN ELECTRIC VEHICLE,”the entirety of which is incorporated herein by reference.

FIELD OF THE INVENTION

The present invention generally relates to the field of batteries. Inparticular, the present invention is directed to systems and methods forpredicting degradation of a battery for use in an electric vehicle.

BACKGROUND

Electric vehicles rely on the energy and power from a battery. Asconstant use of the battery results in a gradual degradation of thebattery's maximum capacity and various performances, the electricvehicle may experience reduced performance qualities that may compromisethe safety of all personnel and cargo of the electric vehicle. The risksare paramount for electrically propelled vehicles such as an electricvertical takeoff and landing (eVTOL) aircraft. Testing the level ofdegradation of a battery may be limited to real-life operation of theelectric vehicle which includes operating the electric vehicle and limittesting its battery performances.

SUMMARY OF THE DISCLOSURE

In an aspect a system for predicting a battery lifetime of a batteryduring use in an electric vehicle, the system including a computingdevice communicatively connected to at least a pack monitor unit,wherein the at least a pack monitor unit is configured to detect abattery pack datum of a plurality of battery modules incorporated in abattery pack, the computing device is configured to receive the batterypack datum as a function of the at least a pack monitor unit, generate,as a function of the battery pack datum, a digital twin including abattery pack model associated with the battery pack of the electricvehicle, receive periodic instances of battery pack data, periodicallyupdate the digital twin as a function of the received battery pack data,simulate a virtual representation of the digital twin, and predict thebattery lifetime of the battery pack as a function of the virtualrepresentation.

In another aspect, a method for predicting a battery lifetime of abattery during use in an electric vehicle, the method includingdetecting, by at least a pack monitor unit communicatively connected toa computing device, a battery pack datum of a plurality of batterymodules incorporated in a battery pack, receiving, by the computingdevice, the battery pack datum from the at least a pack monitor unit,generating, as a function of the battery pack datum, a digital twinincluding a battery pack model associated with the battery pack of theelectric vehicle, receiving, by the computing device, periodic instancesof battery pack data, periodically updating, by the computing device,the digital twin as a function of the received battery pack data,simulating a virtual representation of the digital twin, and predicting,by the computing device, the battery lifetime of the battery pack as afunction of the virtual representation.

These and other aspects and features of non-limiting embodiments of thepresent invention will become apparent to those skilled in the art uponreview of the following description of specific non-limiting embodimentsof the invention in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

For the purpose of illustrating the invention, the drawings show aspectsof one or more embodiments of the invention. However, it should beunderstood that the present invention is not limited to the precisearrangements and instrumentalities shown in the drawings, wherein:

FIG. 1 is a block diagram of an exemplary embodiment of system forpredicting degradation of a battery for use in an electric vehicle;

FIG. 2 is a block diagram of an exemplary embodiment of a batterymanagement system;

FIG. 3 is a schematic illustration of an exemplary battery pack;

FIG. 4 is a block diagram of an exemplary battery pack for preventingprogression of thermal runaway between modules;

FIG. 5 is an illustration of a sensor suite in partial cut-off view;

FIG. 6 is a flow diagram of an exemplary method for predictingdegradation of a battery for use in an electric vehicle;

FIG. 7 is a schematic representation of an exemplary electric verticaltake-off and landing vehicle;

FIG. 8 is a block diagram of an exemplary flight controller;

FIG. 9 is a block diagram of an exemplary machine-learning process; and

FIG. 10 is a block diagram of a computing system that can be used toimplement any one or more of the methodologies disclosed herein and anyone or more portions thereof.

The drawings are not necessarily to scale and may be illustrated byphantom lines, diagrammatic representations and fragmentary views. Incertain instances, details that are not necessary for an understandingof the embodiments or that render other details difficult to perceivemay have been omitted.

DETAILED DESCRIPTION

At a high level, aspects of the present disclosure are directed tosystems and methods for predicting degradation of a battery for use inan electric vehicle. In an embodiment, the electric vehicle may includeany electric aircraft such as an electric vertical take-off and landing(eVTOL) aircraft, unmanned aerial vehicle (UAV), drone, and the likethereof. In an embodiment, aspects of the present disclosure can be usedto accurately predict the level of degradation of a battery of anelectric aircraft and any vehicle. Aspects of the present disclosure canbe used to inform users and pilots in advance well before thedegradation of a battery of an electric vehicle is sufficientlyhazardous to operate.

Aspects of the present disclosure can be used to measure a plurality ofbattery data within the battery pack of the electric aircraft itselfbefore transmitting the data to a more complex computing device togenerate a prediction of the degradation of a battery. This is so, atleast in part, to verify if the battery pack is displaying anyabnormalities or malfunctions rather than an issue of batterydegradation resulting from constant usage. Aspects of the presentdisclosure can also be used to detect any physical qualities of thebattery pack and its individual battery modules and/or battery cells.This is so, at least in part, because any battery data from the batterypack may be significant in the estimation of the battery degradation,therefore, essential to the prediction of the battery degradation in thefuture.

Aspects of the present disclosure allow for predicting degradation ofbattery using a simulator. Collecting data in order estimate the levelof battery degradation may require limit testing the electric vehiclewhich may pose risk to the vehicle, personnel, and any cargo onboard.Aspect of the present disclosure can be used to simulate electricvehicle operation using simulated electric vehicles and measure theusage of the simulated batteries of the simulated electric vehicles andestimate the battery degradation and provide a battery degradationprediction based on simulation data. Exemplary embodiments illustratingaspects of the present disclosure are described below in the context ofseveral specific examples.

Referring now to FIG. 1 , a block diagram of an exemplary embodiment ofsystem 100 for predicting degradation of a battery for use in anelectric vehicle is illustrated. In a non-limiting embodiment, theelectric vehicle may include any electric vehicle such as electricaircraft 164. Electric aircraft 164 may include, but not limited to, aneVTOL aircraft. System 100 includes computing device 128. In anon-limiting embodiment, computing device 128 may include a flightcontroller. Computing device 128 may include any computing device asdescribed in this disclosure, including without limitation amicrocontroller, microprocessor, digital signal processor (DSP) and/orsystem on a chip (SoC) as described in this disclosure. Computing devicemay include, be included in, and/or communicate with a mobile devicesuch as a mobile telephone or smartphone. Computing device 128 mayinclude a single computing device operating independently, or mayinclude two or more computing device operating in concert, in parallel,sequentially or the like; two or more computing devices may be includedtogether in a single computing device or in two or more computingdevices. Computing device 128 may interface or communicate with one ormore additional devices as described below in further detail via anetwork interface device. Network interface device may be utilized forconnecting computing device 128 to one or more of a variety of networks,and one or more devices. Examples of a network interface device include,but are not limited to, a network interface card (e.g., a mobile networkinterface card, a LAN card), a modem, and any combination thereof.Examples of a network include, but are not limited to, a wide areanetwork (e.g., the Internet, an enterprise network), a local areanetwork (e.g., a network associated with an office, a building, a campusor other relatively small geographic space), a telephone network, a datanetwork associated with a telephone/voice provider (e.g., a mobilecommunications provider data and/or voice network), a direct connectionbetween two computing devices, and any combinations thereof. A networkmay employ a wired and/or a wireless mode of communication. In general,any network topology may be used. Information (e.g., data, softwareetc.) may be communicated to and/or from a computer and/or a computingdevice. Computing device 128 may include but is not limited to, forexample, a computing device or cluster of computing devices in a firstlocation and a second computing device or cluster of computing devicesin a second location. Computing device 128 may include one or morecomputing devices dedicated to data storage, security, distribution oftraffic for load balancing, and the like. Computing device 128 maydistribute one or more computing tasks as described below across aplurality of computing devices of computing device, which may operate inparallel, in series, redundantly, or in any other manner used fordistribution of tasks or memory between computing devices. computingdevice 128 may be implemented using a “shared nothing” architecture inwhich data is cached at the worker, in an embodiment, this may enablescalability of system 100 and/or computing device.

With continued reference to FIG. 1 , computing device 128 may bedesigned and/or configured to perform any method, method step, orsequence of method steps in any embodiment described in this disclosure,in any order and with any degree of repetition. For instance, computingdevice 128 may be configured to perform a single step or sequencerepeatedly until a desired or commanded outcome is achieved; repetitionof a step or a sequence of steps may be performed iteratively and/orrecursively using outputs of previous repetitions as inputs tosubsequent repetitions, aggregating inputs and/or outputs of repetitionsto produce an aggregate result, reduction or decrement of one or morevariables such as global variables, and/or division of a largerprocessing task into a set of iteratively addressed smaller processingtasks. computing device 128 may perform any step or sequence of steps asdescribed in this disclosure in parallel, such as simultaneously and/orsubstantially simultaneously performing a step two or more times usingtwo or more parallel threads, processor cores, or the like; division oftasks between parallel threads and/or processes may be performedaccording to any protocol suitable for division of tasks betweeniterations. Persons skilled in the art, upon reviewing the entirety ofthis disclosure, will be aware of various ways in which steps, sequencesof steps, processing tasks, and/or data may be subdivided, shared, orotherwise dealt with using iteration, recursion, and/or parallelprocessing.

With continued reference to FIG. 1 , computing device 128 and/or theflight controller may be controlled by one or moreProportional-Integral-Derivative (PID) algorithms driven, for instanceand without limitation by stick, rudder and/or thrust control lever withanalog to digital conversion for fly by wire as described herein andrelated applications incorporated herein by reference. A “PIDcontroller”, for the purposes of this disclosure, is a control loopmechanism employing feedback that calculates an error value as thedifference between a desired setpoint and a measured process variableand applies a correction based on proportional, integral, and derivativeterms; integral and derivative terms may be generated, respectively,using analog integrators and differentiators constructed withoperational amplifiers and/or digital integrators and differentiators,as a non-limiting example. A similar philosophy to attachment of flightcontrol systems to sticks or other manual controls via pushrods and wiremay be employed except the conventional surface servos, steppers, orother electromechanical actuator components may be connected to thecockpit inceptors via electrical wires. Fly-by-wire systems may bebeneficial when considering the physical size of the aircraft, utilityof for fly by wire for quad lift control and may be used for remote andautonomous use, consistent with the entirety of this disclosure. Thecomputing device may harmonize vehicle flight dynamics with besthandling qualities utilizing the minimum amount of complexity whether itbe additional modes, augmentation, or external sensors as describedherein.

With continued reference to FIG. 1 , system 100 includes battery pack104. In a non-limiting embodiment, system 100 may include a plurality ofbattery packs 104. A “battery pack,” for the purpose of this disclosure,is a set of any number of individual battery modules 124 or identicalbattery modules. A “battery module”, for the purpose of this disclosure,is a source of electric power consisting of one or more electrochemicalcells. Battery pack 104 may include a plurality of battery cells. In anon-limiting embodiment, battery module 124 may include a battery celland/or a plurality of battery cells. In a non-limiting embodiment,battery module 124 may be electrically connected to another batterymodule of a plurality of battery modules. “Electrical connection,” forthe purpose of this disclosure, is a link that allows of the transfer ofelectrical energy from one electric device to another. For example andwithout limitation, battery modules 124 may work in tandem with each topower a flight component. For example and without limitation, a batterymodule may compensate for a faulty battery module. In a non-limitingembodiment, may include at least a cell, such as a chemoelectrical,photo electric, or fuel cell. Battery pack 104 may include, withoutlimitation, a generator, a photovoltaic device, a fuel cell such as ahydrogen fuel cell, direct methanol fuel cell, and/or solid oxide fuelcell, or an electric energy storage device; electric energy storagedevice may include without limitation a capacitor, an inductor, anenergy storage cell and/or a battery. Plurality of battery packs 104 maybe capable of providing sufficient electrical power for auxiliary loads,including without limitation lighting, navigation, communications,de-icing, steering or other systems requiring power or energy. Pluralityof battery packs 104 may be capable of providing sufficient power forcontrolled descent and landing protocols, including without limitationhovering descent or runway landing. Plurality of battery packs mayinclude a device for which power that may be produced per unit of volumeand/or mass has been optimized, at the expense of the maximal totalspecific energy density or power capability, during design. Plurality ofbattery packs 104 may be used, in an embodiment, to provide electricalpower to an electric aircraft or drone, such as an electric aircraftvehicle, during moments requiring high rates of power output, includingwithout limitation takeoff, landing, thermal de-icing and situationsrequiring greater power output for reasons of stability, such as highturbulence situations. In a non-limiting embodiment, battery pack 104may include a plurality of electrochemical cells. In a non-limitingembodiment, battery pack 104 may be configured to deliver electricalpower to a plurality of electrical systems of an electric aircraft. In anon-limiting embodiment, each battery pack 104 of the plurality ofbattery packs may work in tandem to provide electrical energy to aplurality of electrical systems of an electric aircraft. For example andwithout limitation, battery pack 104 may be used to power a flightcomponent or a set of flight components. For example and withoutlimitation, each battery pack 104 may be used to power unique flightcomponents or a unique set of flight components. A “flight component”,for the purposes of this disclosure, is any component related to, andmechanically connected to an aircraft that manipulates a fluid medium inorder to propel and maneuver the aircraft through the fluid medium. Forexample and without limitation, a flight component may include,propellers, vertical propulsors, forward pushers, landing gears,rudders, motors, rotors, and the like thereof. Battery pack 104 mayinclude a battery management system integrated into the battery pack.For instance and without limitation, battery management system may beconsistent with the disclosure of any battery management system in U.S.patent application Ser. No. 17/128,798 and title SYSTEMS AND METHODS FORA BATTERY MANAGEMENT SYSTEM INTEGRATED IN A BATTERY PACK CONFIGURED FORUSE IN ELECTRIC AIRCRAFT,” which is incorporated herein by reference inits entirety. Persons skilled in the art, upon reviewing the entirety ofthis disclosure, will be aware of the various flight components that mayrepresent battery pack 104 consistently with this disclosure.

With continued reference to FIG. 1 , battery pack 104 may include atleast a pack monitor unit (PMU). A “pack monitor unit,” for the purposeof this disclosure, is a measuring device configured to captureinformation regarding a battery pack including battery modules. In anon-limiting embodiment, the at least a PMU may include one or more PMUswherein each PMU is configured to detect information. In a non-limitingembodiment, the plurality of PMUs may be used to compare if the datadetected is the same to confirm if at least one of the PMUs is faulty.In a non-limiting embodiment, the plurality of PMUs may be used tomeasure volatile data in which multiple PMUs may assist in capturing anyrapid changes of a data. In a non-limiting embodiment, the at least aPMU may include a sensor. A “sensor,” for the purposes of thisdisclosure, is an electronic device configured to detect, capture,measure, or combination thereof, a plurality of external and electricvehicle component quantities. The sensor may be integrated and/orconnected to at least an actuator, a portion thereof, or anysubcomponent thereof. The sensor may include a photodiode configured toconvert light, heat, electromagnetic elements, and the like thereof,into electrical current for further analysis and/or manipulation. Thesensor may include circuitry or electronic components configured todigitize, transform, or otherwise manipulate electrical signals.Electrical signals may include analog signals, digital signals, periodicor aperiodic signal, step signals, unit impulse signal, unit rampsignal, unit parabolic signal, signum function, exponential signal,rectangular signal, triangular signal, sinusoidal signal, sinc function,or pulse width modulated signal. The plurality of datum captured by thesensor may include circuitry, computing devices, electronic componentsor a combination thereof that translates into at least an electronicsignal configured to be transmitted to another electronic component. Thesensor may be disposed on at least an actuator of the electric aircraft.An “actuator,” for the purpose of this disclosure, is any flightcomponent or any part of an electric aircraft that helps it to achievephysical movements by converting energy, often electrical, air, orhydraulic, into mechanical force and enable movement. “Disposed,” forthe purpose of this disclosure, is the physical placement of a computingdevice on an actuator. In a non-limiting embodiment, actuator mayinclude a flight component. In a non-limiting embodiment, the sensor mayinclude a plurality of individual sensors disposed on each actuator ofthe electric aircraft. In a non-limiting embodiment, the sensor may bemechanically and communicatively connected to one or more throttles. Thethrottle may be any throttle as described herein, and in non-limitingexamples, may include pedals, sticks, levers, buttons, dials, touchscreens, one or more computing devices, and the like. Additionally, aright-hand floor-mounted lift lever may be used to control the amount ofthrust provided by the lift fans or other propulsors. The rotation of athumb wheel pusher throttle may be mounted on the end of this lever andmay control the amount of torque provided by the pusher motor, or one ormore other propulsors, alone or in combination. Any throttle asdescribed herein may be consistent with any throttle described in U.S.patent application Ser. No. 16/929,206 and titled, “Hover and ThrustControl Assembly for Dual-Mode Aircraft”, which is incorporated hereinin its entirety by reference. The sensor may be mechanically andcommunicatively connected to an inceptor stick. The pilot input mayinclude a left-hand strain-gauge style STICK for the control of roll,pitch and yaw in both forward and assisted lift flight. A 4-way hatswitch on top of the left-hand stick enables the pilot to set roll andpitch trim. Inceptor stick may include any inceptor stick as describedin the entirety of this disclosure. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware variousembodiments and functions of a pilot input and inceptor stick forpurposes as described herein.

With continued reference to FIG. 1 , in a non-limiting embodiment, theat least a PMU may include first PMU 108. In a non-limiting embodiment,the at least a PMU may include second PMU 116. A “first pack monitorunit,” for the purpose of this disclosure, is a device configured tocapture information regarding a battery pack. A “second pack monitorunit,” for the purpose of this disclosure, is a device configured tocapture information regarding a battery pack. In a non-limitingembodiment, first PMU 108 and second PMU 116 may be identical. In anon-limiting embodiment, second PMU 116 may be configured to detect dataafter first PMU 108. First PMU 108 and/or second PMU 116 may include amicrocontroller. The first PMU 108 and/or second PMU 116 may include asensor wherein the sensor may include any sensor as described herein.First PMU 108 may include a first sensor suite. First sensor suite mayinclude a plurality of individual sensors. Second PMU 116 may include asecond sensor suite. Second sensor suite may include a plurality ofindividual sensors. In a non-limiting embodiment, first sensor suite maybe identical to the second sensor suite. First PMU 108 and/or second PMU116 may include a temperature sensor, digital temperature sensor,temperature probe, thermistors, thermocouples, and the like thereof. A“temperature sensor,” for the purposes of this disclosure, is a devicethat detects and measure hotness and coolness of a battery pack 104 andconverts it into electrical signals. In a non-limiting embodiment, firstPMU 108 may be identical to second PMU 116. In a non-limitingembodiment, first PMU 108 and second PMU 116 may be configured tomeasure identical targets. In a non-limiting embodiment, the at leastfirst PMU may measure a different datum of a target the at least asecond monitor unit may measure. In a non-limiting embodiment, first PMU108 and/or second PMU 116 may be used to double check measuring ofdatum. In a non-limiting embodiment, first PMU 108 may be configured todetect first battery pack datum 112 initially and second PMU 116 may beconfigured to detect a second battery pack datum 120 after a timeinterval and/or buffer. For example and without limitation, second PMU116 may be configured to begin detecting a second battery pack datum 120ten nanoseconds after first PMU 108 detects the first battery pack datum112 For example and without limitation, second PMU 116 may be configuredto begin detecting a second battery pack datum 120 sixty seconds afterfirst PMU 108 detects the first battery pack datum 112 Persons skilledin the art, upon reviewing the entirety of this disclosure, will beaware of the various purposes of detecting with a time buffer consistentwith this disclosure.

With continued reference to FIG. 1 , the at least a PMU may beconfigured to detect a plurality of energy data. the at least a PMU maybe configured to detect a battery pack datum. A “battery pack datum,”for the purpose of this disclosure, is an element of data representingphysical attributes of a battery pack. The battery pack datum mayinclude an element of data representative of one or more characteristicscorresponding to at least a portion of the plurality of battery packsand/or the plurality of battery modules. In a non-limiting embodiment,the battery pack datum may include at least an electrical parameterwhich may include, without limitation, voltage, current, impedance,resistance, and/or temperature. The battery pack datum may include astate of charge of the battery pack. A “state of charge,” for thepurpose of this disclosure, is a level of charge relative to capacity,for instance the state of charge may be represented proportionally or asa percentage. The battery pack datum may include a state of health ofthe battery pack. A “state of health,” for the purpose of thisdisclosure, is a figure of merit compared to ideal conditions. In somecases, the state of health may be represented as a percentage, forexample percentage of battery conditions matching batteryspecifications. Current may be measured by using a sense resistor inseries with the circuit and measuring the voltage drop across theresister, or any other suitable instrumentation and/or methods fordetection and/or measurement of current. Voltage may be measured usingany suitable instrumentation or method for measurement of voltage,including methods for estimation as described in further detail below.Each of resistance, current, and voltage may alternatively oradditionally be calculated using one or more relations between impedanceand/or resistance, voltage, and current, for instantaneous,steady-state, variable, periodic, or other functions of voltage,current, resistance, and/or impedance, including without limitationOhm's law and various other functions relating impedance, resistance,voltage, and current with regard to capacitance, inductance, and othercircuit properties. In a non-limiting embodiment, the battery pack datummay include first battery pack datum 112 and second battery pack datum120 detected by first PMU 108 and second PMU 116 respectively.

With continued reference to FIG. 1 , first PMU 108 may be configured todetect first battery pack datum 112. Second PMU 116 may be configured todetect a second battery pack datum 120. A “first battery pack datum,”for the purpose of this disclosure, is an element of data representingphysical attributes of a battery pack detected by first PMU 108. A“second battery pack datum,” for the purposes of this disclosure, is anelement of data representing physical attributes of a battery packdetected by second PMU 116. In a non-limiting embodiment, first batterypack datum 112 and second battery pack datum 120 may be identical. In anon-limiting embodiment, second battery pack datum 120 may be detectedafter first battery pack datum 112. First battery pack datum 112 and/orsecond battery pack datum 120 may include an identical datum ofinformation. Any datum or signal herein may include an electricalsignal. Electrical signals may include analog signals, digital signals,periodic or aperiodic signal, step signals, unit impulse signal, unitramp signal, unit parabolic signal, signum function, exponential signal,rectangular signal, triangular signal, sinusoidal signal, sinc function,or pulse width modulated signal. The plurality of datum captured bysensor may include circuitry, computing devices, electronic componentsor a combination thereof that translates into at least an electronicsignal configured to be transmitted to another electronic component.First battery pack datum 112 and/or second battery pack datum 120 mayinclude identification numbers for a battery pack unit 104 of aplurality of battery pack units. In a non-limiting embodiment, computingdevice 132 may assign first battery pack datum 112 and/or second batterypack datum 120 to a unique battery pack unit 104. First battery packdatum 112 and/or second battery pack datum 120 may include informationdescribing, but not limited to, a voltage, resistance, current,impedance, distance traveled, and the like thereof. In a non-limitingembodiment, first battery pack datum 112 may be different from secondbattery pack datum 120. For example and without limitation, firstbattery pack datum 112 may include a voltage of a battery pack 104 to be700 volts while second battery pack datum 120 may include a voltage of abattery pack 104 to be 600 volts. For example and without limitation,first battery pack datum 112 may include a current of a battery pack 104to be 100 kWh while second battery pack datum 120 may include a voltageof a battery pack 104 to be 70 kWh volts. First battery pack datum 112and/or second battery pack datum 120 may include a temperature datum. A“temperature datum,” for the purposes of this disclosure, is any datumor element of data describing the temperature of a battery pack.Temperature datum may include a heating parameter and a coolingparameter. Heating parameter may include a rate of temperature increaseof a battery pack 104. Cooling parameter may include a rate oftemperature decrease of a battery pack 104. For example and withoutlimitation, temperature datum may include a temperature of 10 to 70degrees Fahrenheit. For example and without limitation, coolingparameter may include a temperature of a battery to be any temperaturebelow 40 degrees Fahrenheit. For example and without limitation, heatingparameter may include a temperature of a battery to be any temperatureabove 100 degrees Fahrenheit. In a non-limiting embodiment, thetemperature datum of first battery pack datum 112 may be different fromthe temperature datum of second battery pack datum 120. For example andwithout limitation, temperature datum of first battery pack datum 112may include a temperature of a battery pack 104 to be 60 degreesFahrenheit while second battery pack datum 120 may include a temperatureof battery pack 104 to be 100 degrees Fahrenheit. In a non-limitingembodiment, first battery pack datum 112 and/or second battery packdatum 120 may include total flight hours that battery pack 104 and/orelectric aircraft have been operating. The first battery pack datumand/or second battery pack datum 120 may include total energy flowedthrough battery pack 104. The first battery pack datum and/or secondbattery pack datum 120 may include a maintenance history of the batterypack 104. The first battery pack datum and/or second battery pack datum120 may include an upper voltage threshold. The first battery pack datumand/or second battery pack datum 120 may include a lower voltagethreshold. The first battery pack datum and/or second battery pack datum120 may include a moisture level threshold.

With continued reference to FIG. 1 , first PMU 108 and/or second PMU 116may include an isolated and/or physical controller area network (CAN)buses connected to the electric aircraft. A “physical controller areanetwork bus,” as used in this disclosure, is vehicle bus unit includinga central processing unit (CPU), a CAN controller, and a transceiverdesigned to allow devices to communicate with each other's applicationswithout the need of a host computer which may be located physically atthe aircraft. Physical controller area network (CAN) bus unit mayinclude physical circuit elements that may use, for instance and withoutlimitation, twisted pair, digital circuit elements/FGPA,microcontroller, or the like to perform, without limitation, processingand/or signal transmission processes and/or tasks. For instance andwithout limitation, CAN bus unit may be consistent with disclosure ofCAN bus unit in U.S. patent application Ser. No. 17/218,342 and titled“METHOD AND SYSTEM FOR VIRTUALIZING A PLURALITY OF CONTROLLER AREANETWORK BUS UNITS COMMUNICATIVELY CONNECTED TO AN AIRCRAFT,” which isincorporated herein by reference in its entirety. In a non-limitingembodiment, first PMU 108 may transmit first battery pack datum 112 tocomputing device 128 as a function of a first physical CAN bus unit. Ina non-limiting embodiment, second PMU 116 may transmit second batterypack datum 120 to computing device 128 as a function of a second CAN busunit. Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of the plurality of physical CAN bus unitrepresenting the first CAN bus unit and the second CAN bust unitconsistent with this disclosure. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of the variousembodiments of transferring data as a function of one or more mediumsfor the purposes as described herein.

With continued reference to FIG. 1 , computing device 128 may beconfigured to receive the battery pack datum which may include firstbattery pack datum 112 and second battery pack datum 120. Computingdevice 128 may be configured to communicatively connect to network 156.A “network”, for the purpose of this disclosure, is any mediumconfigured to facilitate communication between two or more devices.Network 156 may include any network described in this disclosure, forexample without limitation an avionic mesh network. In some cases,network 156 may include a mesh network. A mesh network may include,without limitation, an avionic mesh network. For instance and withoutlimitation, the avionic mesh network may be consistent with the avionicmesh network in U.S. patent application Ser. No. 17/348,916 and titled“METHODS AND SYSTEMS FOR SIMULATED OPERATION OF AN ELECTRIC VERTICALTAKE-OFF AND LANDING (EVTOL) AIRCRAFT,” which is incorporated herein byreference in its entirety. In some embodiments, network 156 may includean intra-aircraft network and/or an inter-aircraft network.Intra-aircraft network may include any intra-aircraft network describedin this disclosure. Inter-aircraft network may include anyinter-aircraft network described in this disclosure.

With continued reference to FIG. 1 , computing device 128 may beconfigured to generate, as a function of the battery pack datum such asfirst battery pack datum 112 and second battery pack datum 120, batterypack model 140. A “battery pack model,” for the purpose of thisdisclosure, is a simulation and/or model of battery pack 104 of abattery pack such as, but not limited to, a battery pack incorporated inan electric aircraft that embodies an analytical and/or interactivevisualization regarding operation and/or performance capabilities ofbattery pack 104. In a non-limiting embodiment, battery pack model 140may include a simulation of battery pack 104 of a simulated vehicle. Ina non-limiting embodiment, battery pack model may include one or more ofa machine-learning model, a mathematical model, an empirical mode, aprobabilistic model, an analytical model, and the like. Battery packmodel 140 may be generated using digital twin 144. A “digital twin,” forthe purpose of this disclosure, is an up-to-date virtual representationof a physical component or process, for instance and without limitationan aircraft such as the electric vehicle. For instance and withoutlimitation, digital twin 144 may be consistent with digital twin in U.S.patent application Ser. No. 17/348,916 and titled “METHODS AND SYSTEMSFOR SIMULATED OPERATION OF AN ELECTRIC VERTICAL TAKE-OFF AND LANDING(EVTOL) AIRCRAFT” which is incorporated herein in its entirety.

With continued reference to FIG. 1 , computing device 128 may beconfigured to receive periodic instances of battery pack data andperiodically update the battery pack model as a function of the periodicinstances of battery pack data. A “battery pack data,” for the purposeof this disclosure, is any element describing information of batterypack 104 and its components. In a non-limiting embodiment, the batterypack data may include a plurality of data captured in different timeintervals. In a non-limiting embodiment, the battery pack data mayinclude the battery pack datum, wherein the battery pack datum mayinclude any battery pack datum described herein, such as first batterypack datum 112 and second battery pack datum 120. For example andwithout limitation, the at least a pack monitor unit may capture thebattery pack data every 5 minutes. In another non-limiting example, theat least a pack monitor unit may capture the battery pack data atdifferent modes or stages of the flight of the electric aircraft. The atleast a pack monitor unit may capture the battery pack data at lift off,forward flight, and landing. For example and without limitation, the atleast a pack monitor unit may measure the battery pack data when theelectric aircraft transitions between vertical wing flight and fixedwing flight. Persons skilled in the art, upon reviewing the entirety ofthis disclosure, will be aware of the various stages, modes, andconfigurations of an electric aircraft and its flight for purposes asdescribed herein.

With continued reference to FIG. 1 , computing device 128 mayincorporate the use of a timer module configured to provide specifictime intervals for the at least a pack monitor unit to capture thebattery pack data. A “timer module,” for the purpose of this disclosure,is any computing device that is used to track time or count down time.In a non-limiting embodiment, the timer module may include any alarmdevice, stopwatch, and the like thereof. In a non-limiting embodiment, auser may manipulate the timer module and set specific time intervals forthe timer module to instruct the capturing of periodic instances of thebattery pack data in various stages of the electric aircraft in aflight. Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of the various embodiments of a timer modulefor purposes as described herein. Additionally or alternatively, batterypack model 140 may be generated as a function of the at least a batterydatum, but battery pack model 140 may remain constant throughout aflight of electric aircraft 164. For example and without limitation,computing device 128 may be receive the at least a battery datum at theinitial start-up of the electric aircraft, wherein battery pack model140 may be generated based on the at least a battery datum received atthe start-up. Battery pack model 140 may represent a visualrepresentation of battery pack 104 of electric aircraft 164 at theinitial start-up, but battery pack 104 may experience various phenomena,such as power consumption, increase in temperature, etc. Battery packmodel 140 may remain constant throughout the flight of electric aircraft164 despite changes to the at least a battery datum. This is so, atleast in part, to provide a simple visual representation of battery pack104 to the pilot instead of a complicated and dynamic model constantlybeing modified by computing device 128 and/or simulator machine 132,which may distract the pilot. While battery pack model 140 may beconstant and not necessarily modeling an accurate representation of thestate of battery pack 104 continuously, computing device 128 maycontinuously receive parameters from battery pack 104 such as the atleast a battery datum and generate battery degradation prediction 152,wherein battery degradation prediction 152 may be presented as acomputer-readable format, electronic signals and/or files, etc., foranalysis purposes. In a non-limiting embodiment, computing device 128may use the timer module to generate battery pack model 140 at varioustime intervals instead of continuously modifying the battery pack modeland generating updated models as a function of continuous receiving ofinputs. In another non-limiting embodiment, battery pack model 140 mayinclude any algorithm as described in the entirety of this disclosure.

With continued reference to FIG. 1 , simulator machine 132 may beconfigured to simulate virtual representation 136 of digital twin 144.As described in this disclosure, a “virtual representation” includes anymodel or simulation accessible by computing device which isrepresentative of a physical phenomenon, for example without limitationat least a part of an electric vehicle such as an electric vehicleand/or its battery pack, or a simulator module. For instance and withoutlimitation, the virtual representation 136 may be consistent withvirtual representation in U.S. patent application Ser. No. 17/348,916and titled “METHODS AND SYSTEMS FOR SIMULATED OPERATION OF AN ELECTRICVERTICAL TAKE-OFF AND LANDING (EVTOL) AIRCRAFT” which is incorporatedherein in its entirety. In some cases, virtual representation may beinteractive with simulator machine 132. Simulator machine 132 mayinclude a flight simulator. For instance and without limitation,simulator machine 132 may be consistent with flight simulator in U.S.patent application Ser. No. 17/348,916 and titled “METHODS AND SYSTEMSFOR SIMULATED OPERATION OF AN ELECTRIC VERTICAL TAKE-OFF AND LANDING(EVTOL) AIRCRAFT” which is incorporated herein in its entirety. Forexample, in some cases, data may originate from virtual representation136 and be input into simulator machine 132. Alternatively oradditionally, in some cases, virtual representation 136 may modify ortransform data already available to simulator machine 132. Virtualrepresentation 136 may include digital twin 144 of at least an aircraftcomponent 116. Aircraft digital twin 144 may include any digital twin asdescribed in this disclosure, for example below. In some cases, at leastan aircraft component 116 includes an electric vertical take-off andlanding (eVTOL) aircraft, for example a functional flight-worthy eVTOLaircraft; and aircraft digital twin 144 is a digital twin of the eVTOLaircraft. In some cases, at least a virtual representation 136 mayinclude a virtual controller area network. Virtual controller areanetwork may include any virtual controller area network as described inthis disclosure, for example below. In some cases, aircraft digital twinmay include a flight controller model. Flight controller model mayinclude any flight controller model described in this disclosure.

With continued reference to FIG. In some embodiments, digital twin 144may include a battery pack model 140. Battery pack model 140 may includeany model related to at least property, characteristic, or function of abattery located within aircraft. In some cases, battery pack model 140may include a model of a battery controller, management, and/ormonitoring system. Disclosure related to battery management for eVTOLaircraft may be found in patent application Ser. Nos. 17/108,798 and17,111,002, entitled “PACK LEVEL BATTERY MANAGEMENT SYSTEM” and“ELECTRICAL DISTRIBUTION MONITORING SYSTEM FOR AN ELECTRIC AIRCRAFT,”respectively, each of which is incorporated herein by reference in itsentirety. In some cases, battery pack model 140 may include anelectrochemical model of battery, which may be predictive of energyefficiencies and heat generation and transfer of at least a battery. Insome cases, battery pack model 140 may be configured to predict batterylifetime, given known battery parameters, for example measured batteryperformance, temperature, utilization, and the like. In a non-limitingembodiment, battery pack model 140 may include an integratedmulti-physics, multiscale, probabilistic simulation of an as-builtvehicle or system that uses best available physical models, sensorupdates, fleet history, and the like, to mirror the electric vehicle orat least the electric vehicle's component, for instance battery. In somecases, digital twin digital twin may be a virtual instance of theelectric vehicle or the battery that is continually updated with thevehicle or battery's performance, maintenance, and health status data,for example throughout the vehicle or battery's life cycle. For exampleand without limitation, battery pack model 140 may further includeprevious prediction data associated with battery pack 104.

With continued reference to FIG. 1 , in some embodiments, digital twin144 may model, simulate, predict, and/or determine an aspect of electricaircraft 156 using machine-learning processes, including anymachine-learning process described in this application. Digital twin 144may include analytical models, for example those based upon knownphysical laws and phenomena, such as Newton's laws of motion.Alternatively and/or additionally, digital twin 144 may includedata-driven models based largely on observed data, for exampleMonte-Carlo modeling and/or machine-learning processes. In some cases,digital twin 144 may be constituted of digital threads. According tosome embodiments, a digital thread may be considered a lowest leveldesign and specification for a digital representation of a physicalitem. Use of digital threads may, in some cases, ensure deep coherencebetween models of a digital twin 144. In some cases, a digital twin 144may include a design equation and/or design matrix. A design equationmay mathematically represent some or all design requirements andparameters associated with a particular design, for example an electricaircraft 156.

With continued reference to FIG. 1 , battery pack model furthercomprises a plurality of virtual instances of the battery pack datumupdated continuously. A “plurality of virtual instances,” for thepurpose of this disclosure, is a plurality of virtual representations136 of battery pack 104 simulated in time intervals. In a non-limitingembodiment, the plurality of virtual instances may be used in whichcomputing device 128 may calculate battery degradation estimation 148which may be further used to generate battery degradation prediction. A“battery degradation estimation,” is any element of data describing avalue or rate describing the degrading capabilities of a battery pack ofan electric aircraft. In a non-limiting embodiment, computing device 128may calculate battery degradation estimation 138 continuously and/ordynamically throughout a duration of an operation of electric aircraft156. In a non-limiting embodiment, computing device 128 may calculatebattery degradation estimation 148. In a non-limiting embodiment, theplurality of virtual instances may include a plurality of battery packmodels recorded from previous simulations of flights and/or operationsof electric aircraft 156. For example and without limitation, asimulated electric aircraft operated by a simulator module may generatea simulated battery pack model of the simulated electric aircraft afterthe flight and landing of the simulated electric aircraft inside asimulated environment. The simulator module may operate the samesimulated electric aircraft in which battery pack of the simulatedelectric aircraft may degrade after a plurality of simulated flights inwhich computing device 128 may use the plurality of virtual instances ofsimulated battery pack models from the plurality of simulated flights ofthe simulated electric aircraft to generate battery degradationestimation 148 and/or battery degradation prediction 152. In someembodiment, battery pack model 140 may include a plurality of batterypack models which may include a plurality of virtual representations ofa real-life operation of an aircraft such as electric aircraft 156.Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of the various embodiments of simulated modelsof data from real-life operation of a physical electric aircraft 156 ora simulation data from a simulated electric aircraft for purposesdescribed herein. In a non-limiting embodiment, computing device 128 mayuse data from both real-life data and simulation data for purposesdescribed herein.

With continued reference to FIG. 1 , system 100 may include a simulatormodule. The simulator module may be communicatively connected tocomputing device 128 by any communication means described in thisdisclosure, for example without limitation network 112. As used in thisdisclosure, a “simulator module” is a physical component that is asimulation of an aircraft component. Simulator module may include actualaircraft components that have been separated from a functioning aircraftor otherwise de-activated. A simulator module may include a model orreplica. In some cases, simulator module may include a physical twin ofat least an aircraft component. In some cases, simulator module mayinclude a physical cockpit. The physical cockpit may include at least anaircraft component. For example, a physical cockpit may include one ormore of an aircraft interior, seating, windows, displays, pilotcontrols, and the like. The physical cockpit may be used to perform asimulated flight mission. As used in this disclosure, a “simulatedflight mission” is any use of a simulator machine 132 that includes asimulated flight. The simulator module and/or physical cockpit mayinclude at least a pilot control configured to interface with a user.The at least a pilot control may include any pilot control described inthis disclosure. In some cases, at least one of the simulator module,the physical cockpit, and a pilot control may include at least a sensor.The at least a sensor may be communicatively connected to computingdevice 128. In some cases, the at least a sensor may be configured todetect a user interaction with the at least a pilot control. The at aleast a sensor may include any sensor described in this disclosure.

With continued reference to FIG. 1 , computing device 128 is configuredto determine battery degradation prediction 152 as a function of thebattery pack datum, which may further include first battery pack datum112 and second battery pack datum 120, and battery pack model 140. A“battery degradation prediction,” for the purpose of this disclosure, isa model, simulation, or element of data representing the degradation ofbattery pack 104 of an electric vehicle which may include electricaircraft 156. For example and without limitation, battery degradationprediction 152 may include a virtual representation of a battery packmodel representing a potential degraded state of battery pack 104. Forexample and without limitation, battery degradation prediction 152 mayinclude a model such as a battery degradation model. A “batterydegradation model,” for the purpose of this disclosure, is any virtualrepresentation and/or battery pack model representing a predictivebattery pack model describing a future degraded state of battery pack104. In a non-limiting embodiment, the battery degradation model mayinclude a graphical representation depicting the state of charge ofbattery pack 104 as a function of time. For instance and withoutlimitation, the battery degradation model may be consistent with FIG. 7, a graph showing the state of charge of an energy source as a functionof time, in U.S. patent application Ser. No. 16/599,538 and titled“SYSTEMS AND METHODS FOR IN-FLIGHT OPERATIONAL ASSESSMENT,” which isincorporated in its entirety herein. In a non-limiting embodiment,computing device 128 may be configured to generate a machine-learningmodel, wherein the machine-learning model is configured to receive thebattery pack datum as an input and output the battery degradation modelas a function of a degradation training set. A “degradation trainingset,” for the purpose of this disclosure, is a training set thatincludes a previous instance of a battery pack datum correlated to thesimulated virtual representation and its battery pack model of thatprevious instance. In a non-limiting embodiment, the degradationtraining set may include battery degradation estimation 148. In anon-limiting embodiment, the previous instance of the battery pack datumand the previous instance of the virtual representation and its batterypack model may be retrieved from battery database 160.

With continued reference to FIG. 1 , a “battery database,” for thepurpose of this disclosure, is a data storage system used to store anydatum received and/or generated by any device within system 100. In anon-limiting embodiment, battery database 160 may include a clouddatabase which may be only accessed by a device such as computing device128 in the event computing device 128 is connected to network 156.Battery database 160 may include a battery pack datum table, a batterypack model table, and/or a battery degradation prediction table. Thebattery pack datum table may include any data table configured to storeany battery pack datum received by computing device 128. In anon-limiting embodiment, computing device 128 may retrieve a batterypack datum from the battery pack datum table of battery database 160 touse, at least in part, as a training set. The battery pack model tablemay include any data table configured to store any battery pack modelthat may be generated by computing device 128. For example and withoutlimitation, the battery pack model table may include previous batterypack models generated in a previous flight and/or operation of electricaircraft 164 and a previous flight and/or operation of a simulatedelectric aircraft. In a non-limiting embodiment, computing device 128may retrieve any battery pack model from the battery pack model table ofbattery database 160 to use, at least in part, as a training set. Thebattery degradation prediction table may include any battery degradationprediction generated by computing device 128. The battery degradationprediction table may include any data table configured to store anybattery degradation prediction generated by computing device 128. Forexample and without limitation, computing device 128 may retrieve anybattery degradation prediction from the battery degradation predictiontable of battery database 160 which may be used, at least in part, as atraining set. Persons skilled in the art, upon reviewing the entirety ofthis disclosure, will be aware of the various data and tables of abattery database for purposes described herein.

With continued reference to FIG. 1 , battery degradation prediction 152including the battery degradation prediction model may include anexpected battery pack model. An “expected battery pack model,” for thepurpose of this disclosure, is any predictive simulation, model, orelement of data describing the degradation of a battery pack of electricaircraft 164 or simulated electric aircraft. The expected battery packmodel may be generated as a function of the machine-learning model.

With continued reference to FIG. 1 , in a non-limiting embodiment, thegeneration of battery degradation prediction 152 and any model and/orany algorithm may be performed by a remote device which may includecomputing device 128. For example and without limitation, electricaircraft 164 and its battery pack 104 may transmit the at least abattery datum via network 156 once connected to the remote devicewherein the remote device may generate battery degradation prediction152 as a function of network 156. This is so that electric aircraft 164may only be responsible for detecting and transmitting any battery packdatum while the remote device may be configured to perform any algorithmand/or any model generation in the cloud of network 156. In anon-limiting embodiment, generation of battery degradation prediction152 may be conducted by a computing device disposed on electric aircraft164. For example and without limitation, the computing device mayperform and algorithm and generate any model or any prediction onceconnected to network 156 which the computing device disposed on electricaircraft 164 may access battery database 160, a cloud database, toretrieve the degradation training set to generate battery degradationprediction 152. In a non-limiting embodiment, the above-mentioned stepsmay be imitated in a simulated environment. Persons skilled in the art,upon reviewing the entirety of this disclosure, will be aware of thevarious embodiments of a computing device performing any computation inthe context of a real-life event and/or simulated environment forpurposes as described herein.

Referring now to FIG. 2 , an embodiment of battery management system 200is presented. Battery management system 200 is be integrated in abattery pack 104 configured for use in an electric aircraft. The batterymanagement system 200 is be integrated in a portion of the battery pack104 or subassembly thereof. Battery management system 200 includes firstbattery management component 204 disposed on a first end of the batterypack. One of ordinary skill in the art will appreciate that there arevarious areas in and on a battery pack and/or subassemblies thereof thatmay include first battery management component 204. First batterymanagement component 204 may take any suitable form. In a non-limitingembodiment, first battery management component 204 may include a circuitboard, such as a printed circuit board and/or integrated circuit board,a subassembly mechanically coupled to at least a portion of the batterypack, standalone components communicatively coupled together, or anotherundisclosed arrangement of components; for instance, and withoutlimitation, a number of components of first battery management component204 may be soldered or otherwise electrically connected to a circuitboard. First battery management component may be disposed directly over,adjacent to, facing, and/or near a battery module and specifically atleast a portion of a battery cell. First battery management component204 includes first sensor suite 208. First sensor suite 208 isconfigured to measure, detect, sense, and transmit first plurality ofbattery pack datum 112 to battery database 160.

Referring again to FIG. 2 , battery management system 200 includessecond battery management component 212. For instance and withoutlimitation, battery management system may be consistent with disclosureof battery management system in U.S. patent application Ser. No.17/108,798 and titled “SYSTEMS AND METHODS FOR A BATTERY MANAGEMENTSYSTEM INTEGRATED IN A BATTERY PACK CONFIGURED FOR USE IN ELECTRICAIRCRAFT,” which is incorporated herein by reference in its entirety.Second battery management component 212 is disposed in or on a secondend of battery pack 104. Second battery management component 212includes second sensor suite 216. Second sensor suite 216 may beconsistent with the description of any sensor suite disclosed herein.Second sensor suite 216 is configured to measure second plurality ofbattery pack datum 120. Second plurality of battery pack datum 120 maybe consistent with the description of any battery pack datum disclosedherein. Second plurality of battery pack datum 120 may additionally oralternatively include data not measured or recorded in another sectionof battery management system 200. Second plurality of battery pack datum120 may be communicated to additional or alternate systems to which itis communicatively coupled. Second sensor suite 216 includes a moisturesensor consistent with any moisture sensor disclosed herein, namelymoisture sensor 208.

With continued reference to FIG. 2 , first battery management component204 disposed in or on battery pack 104 may be physically isolated fromsecond battery management component 212 also disposed on or in batterypack 104. “Physical isolation”, for the purposes of this disclosure,refer to a first system's components, communicative coupling, and anyother constituent parts, whether software or hardware, are separatedfrom a second system's components, communicative coupling, and any otherconstituent parts, whether software or hardware, respectively. In anon-limiting embodiment, the plurality of the first and second batterymanagement component may be outside the battery pack 104. First batterymanagement component 204 and second battery management component 216 mayperform the same or different functions in battery management system200. In a non-limiting embodiment, the first and second batterymanagement components perform the same, and therefore redundantfunctions. If, for example, first battery management component 204malfunctions, in whole or in part, second battery management component216 may still be operating properly and therefore battery managementsystem 200 may still operate and function properly for electric aircraftin which it is installed. Additionally or alternatively, second batterymanagement component 216 may power on while first battery managementcomponent 204 is malfunctioning. One of ordinary skill in the art wouldunderstand that the terms “first” and “second” do not refer to either“battery management components” as primary or secondary. In non-limitingembodiments, first battery management component 204 and second batterymanagement component 216 may be powered on and operate through the sameground operations of an electric aircraft and through the same flightenvelope of an electric aircraft. This does not preclude one batterymanagement component, first battery management component 204, fromtaking over for second battery management component 216 if it were tomalfunction. In non-limiting embodiments, the first and second batterymanagement components, due to their physical isolation, may beconfigured to withstand malfunctions or failures in the other system andsurvive and operate. Provisions may be made to shield first batterymanagement component 204 from second battery management component 216other than physical location such as structures and circuit fuses. Innon-limiting embodiments, first battery management component 204, secondbattery management component 216, or subcomponents thereof may bedisposed on an internal component or set of components within batterypack 104, such as on battery module sense board 160.

Referring again to FIG. 2 , first battery management component 204 maybe electrically isolated from second battery management component 216.“Electrical isolation”, for the purposes of this disclosure, refer to afirst system's separation of components carrying electrical signals orelectrical energy from a second system's components. First batterymanagement component 204 may suffer an electrical catastrophe, renderingit inoperable, and due to electrical isolation, second batterymanagement component 216 may still continue to operate and functionnormally, managing the battery pack of an electric aircraft. Shieldingsuch as structural components, material selection, a combinationthereof, or another undisclosed method of electrical isolation andinsulation may be used, in non-limiting embodiments. For example, arubber or other electrically insulating material component may bedisposed between the electrical components of the first and secondbattery management components preventing electrical energy to beconducted through it, isolating the first and second battery managementcomponents from each other.

With continued reference to FIG. 2 , battery management system 200includes battery database 160. Battery database 160 is configured tostore first plurality of battery pack datum 112 and second plurality ofbattery pack datum 120. Battery database 160 may include a database.Battery database 160 may include a solid-state memory or tape harddrive. Battery database 160 may be communicatively coupled to firstbattery management component 204 and second battery management component212 and may be configured to receive electrical signals related tophysical or electrical phenomenon measured and store those electricalsignals as first battery pack datum 112 and second battery pack datum120, respectively. Alternatively, battery database 160 may include morethan one discrete battery databases that are physically and electricallyisolated from each other. In this non-limiting embodiment, each of firstbattery management component 204 and second battery management component212 may store first battery pack datum 112 and second battery pack datum120 separately. One of ordinary skill in the art would understand thevirtually limitless arrangements of data stores with which batterymanagement system 200 could employ to store the first and secondplurality of battery pack datum.

Referring again to FIG. 2 , battery database 160 stores first pluralityof battery pack datum 112 and second plurality of battery pack datum120. First plurality of battery pack datum 112 and second plurality ofbattery pack datum 120 may include total flight hours that battery pack104 and/or electric aircraft have been operating. The first and secondplurality of battery pack datum may include total energy flowed throughbattery pack 104. Battery database 160 may be implemented, withoutlimitation, as a relational database, a key-value retrieval datastoresuch as a NOSQL database, or any other format or structure for use as adatastore that a person skilled in the art would recognize as suitableupon review of the entirety of this disclosure. Battery database 160 maycontain datasets that may be utilized by an unsupervisedmachine-learning model to find trends, cohorts, and shared datasetsbetween data contained within battery database 160 and first batterypack datum 112 and/or second battery pack datum 120. In an embodiment,datasets contained within battery database 160 may be categorized and/ororganized according to shared characteristics. For instance and withoutlimitation, one or more tables contained within battery database 160 mayinclude first battery pack datum table. First battery pack datum tablemay contain datasets classified to first battery pack information offirst battery pack datum. First battery pack information may includedatasets describing any first battery pack datum as described herein.One or more tables contained within battery database 160 may include asecond battery pack datum table. second battery pack datum table maycontain datasets classified to second battery pack information of secondbattery pack datum. Second battery pack information may include datasetsdescribing any second battery pack datum as described herein. One ormore tables contained within battery database 160 may include acomparison datum table. Comparison datum table may include datasetsclassified by level of comparison between first battery pack datum 112and second battery pack datum 120. Comparison datum table may includedatasets classified by the severity of the difference of the comparisonof the first and second battery pack datum from the differentialthreshold. Battery database 160 may be communicatively coupled tosensors that detect, measure and store energy in a plurality ofmeasurements which may include current, voltage, resistance, impedance,coulombs, watts, temperature, or a combination thereof. Additionally oralternatively, battery database 160 may be communicatively coupled to asensor suite consistent with this disclosure to measure physical and/orelectrical characteristics. Battery database 160 may be configured tostore first battery pack datum 112 and second battery pack datum 120wherein at least a portion of the data includes battery pack maintenancehistory. Battery pack maintenance history may include mechanicalfailures and technician resolutions thereof, electrical failures andtechnician resolutions thereof. Additionally, battery pack maintenancehistory may include component failures such that the overall systemstill functions. Battery database 160 may store the first and secondbattery pack datum that includes an upper voltage threshold and lowervoltage threshold consistent with this disclosure. First battery packdatum 112 and second battery pack datum 120 may include a moisture levelthreshold. The moisture level threshold may include an absolute,relative, and/or specific moisture level threshold. Battery managementsystem 200 may be designed to the Federal Aviation Administration(FAA)'s Design Assurance Level A (DAL-A), using redundant DAL-Bsubsystems.

With continued reference to FIG. 2 , battery management system 200 mayinclude a data collection system, which may include a central sensorsuite which may be used for first sensor suite 208 in first batterymanagement component 104 or second sensor suite 216 in second batterymanagement component 112 or consistent with any sensor suite disclosedhereinabove. Data collection system includes battery database 160.Central sensor suite is configured to measure physical and/or electricalphenomena and characteristics of battery pack 104, in whole or in part.Central sensor suite then transmits electrical signals to batterydatabase 160 to be saved. Those electrical signals are representative offirst battery pack datum 112 and second battery pack datum 120. Theelectrical signals communicated from central sensor suite, and moremoreover from the first or second battery management component 212 towhich it belongs may be transformed or conditioned consistent with anysignal conditioning present in this disclosure. Data collection systemand more specifically first battery management component 104, may beconfigured to save first battery pack datum 112 and second battery packdatum 120 periodically in regular intervals to battery database 160.“Regular intervals”, for the purposes of this disclosure, refers to anevent taking place repeatedly after a certain amount of elapsed time.Data collection system may include first battery management component104, which may include timer 504. Timer 504 may include a timingcircuit, internal clock, or other circuit, component, or part configuredto keep track of elapsed time and/or time of day. For example, innon-limiting embodiments, battery database 160 may save the first andsecond battery pack datum every 30 seconds, every minute, every 30minutes, or another time period according to timer 504. Additionally oralternatively, battery database 160 may save the first and secondbattery pack datum after certain events occur, for example, innon-limiting embodiments, each power cycle, landing of the electricaircraft, when battery pack is charging or discharging, or scheduledmaintenance periods. In non-limiting embodiments, battery pack 104phenomena may be continuously measured and stored at an intermediarystorage location, and then permanently saved by battery database 160 ata later time, like at a regular interval or after an event has takenplace as disclosed hereinabove. Additionally or alternatively, batterydatabase may be configured to save first battery pack datum 112 andsecond battery pack datum 120 at a predetermined time. “Predeterminedtime”, for the purposes of this disclosure, refers to an internal clockwithin battery management system 200 commanding battery database 160 tosave the first and second battery pack datum at that time. For example,battery database 160 may be configured to save the first and secondbattery pack datum at 0600 hours, 11 P.M. EDT, another time, multipletimes a day, and/or the like.

Referring now to the drawings, FIG. 3 illustrates an exemplary batterypack 300. Battery pack 400 may include any battery pack described in theentirety of this disclosure. For instance and without limitation,battery pack 300 may be consistent with battery pack in U.S. patentapplication Ser. No. 17/319,174 and titled “SYSTEMS AND METHODS OF USEFOR A BATTERY PACK ENCLOSURE,” which is incorporated by reference in itsentirety. According to some embodiments, a battery pack 300 includes anouter case 304. In some cases, case 304 may be made from metal forexample one or more of sheet metal, stamped metal, extruded metal,and/or machined metal. In some cases, case 304 may be formed by way ofwelding, brazing, and/or soldering. In some cases, case 304 may becomposed wholly or in part of a relatively light and strong metal, suchas without limitation aluminum alloy. As shown in FIG. 3 , case 304, mayinclude an outer case, which may substantially enclose a plurality ofbattery modules 308A-C. In some versions, case may provide a firewallbetween flammable battery modules within battery pack and an environmentor vehicle surrounding the battery pack.

Continuing in reference to FIG. 3 , Battery modules 308A-C may includeany battery modules or battery cells described throughout thisdisclosure, for instance without limitation those described below.Typically, battery modules 308A-C are connected in series to oneanother, such that a total potential for all of the battery modulestogether is greater than a potential for any one of the battery modules(e.g., 308A). In some cases, a shared electrical connection fromplurality of modules 308A-C may be accessible by way of an electricalconnector 312A-B. In some cases, the electrical connector 312A-B mayhave a polarity and include a positive connection 312A and a negativeconnection 312B. In some cases, one or more battery modules of pluralityof battery modules 308A-C may be mounted to case 304 by way of at leasta breakaway mount 316A-C. In some embodiments, a breakaway mount mayinclude any means for attachment that is configured to disconnect undera predetermined load. In some cases, breakaway mounts may be passive andrely upon loading forces for disconnection, such as exemplary breakawaymounts which may include one or more of a shear pin, a frangible nut, afrangible bolt, a breakaway nut, bolt, or stud, and the like. In somecases, a passive breakaway mount may include a relatively soft orbrittle material (e.g., plastic) which is easily broken under achievableloads. Alternatively or additionally, a breakaway mount may include anotch, a score line, or another weakening feature purposefullyintroduced to the mount to introduce breaking at a prescribed load.According to some embodiments, a canted coil spring may be used to aspart of a breakaway mount, to ensure that the mount disconnects under apredetermined loading condition. In some cases a mount may comprise acanted coil spring, a housing, and a piston; and sizes and profiles ofthe housing and the piston may be selected in order to prescribe a forcerequired to disconnect the mount. An exemplary canted coil spring may beprovided by Bal-Seal Engineering, Inc. of Foothill Ranch, Calif., U.S.A.Alternatively or additionally, a breakaway mount may include an activefeature which is configured to actively disconnect a mount under aprescribed condition (for instance a rapid change in elevation or largemeasured G-forces). Much like an airbag that is configured to activateduring a crash, an active mount may be configured to actively disconnectduring a sensed crash. An active mount may, in some cases, include oneor more of an explosive bolt, an explosive nut, an electro-magneticconnection, and the like. In some cases, one or more breakaway mounts316A-C may be configured to disconnect under a certain loadingcondition, for instance a force in excess of a predetermined threshold(i.e., battery breakaway force) acting substantially along (e.g., withinabout +/−45°) a predetermined direction. Non-limiting exemplary batterybreakaway forces may include decelerations in excess of 4, 32, 20, 50,or 300 G's.

In some embodiments, a case 304 circumscribes an inner volume, which mayinclude a battery storage zone, for instance within which batterymodules 308A-C are located, and a crush zone. As a non-limiting example,crush zone may be located between one or more battery modules 308A-C andan inner wall of case 304. In some embodiments, crush zone may besubstantially empty. Alternatively, in some other embodiments, crushzone may comprise some material, such as without limitation acompressible material. In some cases, compressible material may beconfigured to absorb and/or dissipate energy as it is compressed. Insome cases, compressible material may include a material having a numberof voids; for instance, compressible material may take a form of ahoneycomb or another predictably cellular form. Alternatively oradditionally, compressible material may include a non-uniform material,such as without limitation a foam. In some embodiments, a crush zone maybe located down from one or more battery modules 308A-C substantiallyalong a loading direction, such that for instance the one or morebattery modules when disconnected from one or more breakaway mounts316A-C may be directed toward crush zone. In some cases, case 304 mayinclude one or more channels or guides 320A-C configured to direct atleast a battery module 308A-C into a crush zone, should it becomedisconnected from the case.

Still referring to FIG. 3 , in some embodiments, battery module 308A-Cmay include Li ion batteries which may include NCA, NMC, Lithium ironphosphate (LiFePO4) and Lithium Manganese Oxide (LMO) batteries, whichmay be mixed with another cathode chemistry to provide more specificpower if the application requires Li metal batteries, which have alithium metal anode that provides high power on demand, Li ion batteriesthat have a silicon, tin nanocrystals, graphite, graphene or titanateanode, or the like. Batteries and/or battery modules may include withoutlimitation batteries using nickel-based chemistries such as nickelcadmium or nickel metal hydride, batteries using lithium-ion batterychemistries such as a nickel cobalt aluminum (NCA), nickel manganesecobalt (NMC), lithium iron phosphate (LiFePO4), lithium cobalt oxide(LCO), and/or lithium manganese oxide (LMO), batteries using lithiumpolymer technology, metal-air batteries. Battery modules 308A-C mayinclude lead-based batteries such as without limitation lead acidbatteries and lead carbon batteries. Battery modules 308A-C may includelithium sulfur batteries, magnesium ion batteries, and/or sodium ionbatteries. Batteries may include solid state batteries orsupercapacitors or another suitable energy source. Batteries may beprimary or secondary or a combination of both. Additional disclosurerelated to batteries and battery modules may be found in co-owned U.S.Patent Applications entitled “SYSTEM AND METHOD FOR HIGH ENERGY DENSITYBATTERY MODULE” and “SYSTEMS AND METHODS FOR RESTRICTING POWER TO A LOADTO PREVENT ENGAGING CIRCUIT PROTECTION DEVICE FOR AN AIRCRAFT,” havingU.S. patent application Ser. Nos. 16/948,140 and 16/590,496respectively; the entirety of both applications are incorporated hereinby reference. Persons skilled in the art, upon reviewing the entirety ofthis disclosure, will be aware of various devices of components that maybe used as a battery module. In some cases, case 304 is constructed in amanner that maximizes manufacturing efficiencies.

Referring now to the drawings, FIG. 4 illustrates a block diagram of anexemplary battery pack 400 for preventing progression of thermal runawaybetween modules. In a non-limiting embodiment, battery pack 400 mayinclude any battery pack as described in the entirety of thisdisclosure. For instance and without limitation, battery pack 400 may beconsistent with battery pack in U.S. patent application Ser. No.17/348,960 and titled “BATTERY PACK FOR ELECTRIC VERTICAL TAKE-OFF ANDLANDING AIRCRAFT,” which is incorporated by reference in its entirety.Battery pack 400 may include a pouch cell 404A-B. As used in thisdisclosure, “pouch cell” is a battery cell or module that includes apouch. In some cases, a pouch cell may include or be referred to as aprismatic pouch cell, for example when an overall shape of pouch isprismatic. In some cases, a pouch cell may include a pouch which issubstantially flexible. Alternatively or additionally, in some cases,pouch may be substantially rigid. Pouch cell 404A-B may include at leasta pair of electrodes 408A-B. At least a pair of electrodes 408A-B mayinclude a positive electrode and a negative electrode. Each electrode ofat least a pair of electrodes 408A-B may include an electricallyconductive element. Non-limiting exemplary electrically conductiveelements include braided wire, solid wire, metallic foil, circuitry,such as printed circuit boards, and the like. At least a pair ofelectrodes 408A-B may be in electric communication with and/orelectrically connected to at least a pair of foil tabs 412A-B. At leasta pair of electrodes 408A-B may be bonded in electric communication withand/or electrically connected to at least a pair of foil tabs 412A-B byany known method, including without limitation welding, brazing,soldering, adhering, engineering fits, electrical connectors, and thelike. In some cases, at least a pair of foil tabs may include a cathodeand an anode. In some cases, an exemplary cathode may include alithium-based substance, such as lithium-metal oxide, bonded to analuminum foil tab. In some cases, an exemplary anode may include acarbon-based substance, such as graphite, bonded to a copper tab. Apouch cell 404A-B may include an insulator layer 416A-B. As used in thisdisclosure, an “insulator layer” is an electrically insulating materialthat is substantially permeable to battery ions, such as withoutlimitation lithium ions. In some cases, insulator layer may be referredto as a separator layer or simply separator. In some cases, insulatorlayer 416A-B is configured to prevent electrical communication directlybetween at least a pair of foil tabs 412A-B (e.g., cathode and anode).In some cases, insulator layer 416A-B may be configured to allow for aflow ions across it. Insulator layer 416A-B may consist of a polymer,such as without limitation polyolifine (PO). Insulator layer 416A-B maycomprise pours which are configured to allow for passage of ions, forexample lithium ions. In some cases, pours of a PO insulator layer416A-B may have a width no greater than 400 μm, 40 μm, 4 μm, or 0.1 μm.In some cases, a PO insulator layer 416A-B may have a thickness within arange of 4-400 μm, or 40-50 μm.

With continued reference to FIG. 4 , pouch cell 404A-B may include apouch 420A-B. Pouch 420A-B may be configured to substantially encompassat least a pair of foil tabs 412A-B and at least a portion of insulatorlayer 416A-B. In some cases, pouch 420A-B may include a polymer, such aswithout limitation polyethylene, acrylic, polyester, and the like. Insome case, pouch 420A-B may be coated with one or more coatings. Forexample, in some cases, pouch may have an outer surface coated with ametalizing coating, such as an aluminum or nickel containing coating. Insome cases, pouch coating be configured to electrically ground and/orisolate pouch, increase pouches impermeability, increase pouchesresistance to high temperatures, increases pouches thermal resistance(insulation), and the like. An electrolyte 424A-B is located withinpouch. In some cases, electrolyte 424A-B may comprise a liquid, a solid,a gel, a paste, and/or a polymer. Electrolyte may wet or contact one orboth of at least a pair of foil tabs 412A-B.

With continued reference to FIG. 4 , battery pack 400 may additionallyinclude an ejecta barrier 428. Ejecta barrier may be locatedsubstantially between a first pouch cell 404A and a second pouch cell404B. As used in this disclosure, an “ejecta barrier” is any material orstructure that is configured to substantially block, contain, orotherwise prevent passage of ejecta. As used in this disclosure,“ejecta” is any material that has been ejected, for example from abattery cell. In some cases, ejecta may be ejected during thermalrunaway of a battery cell. Alternatively or additionally, in some cases,eject may be ejected without thermal runaway of a battery cell. In somecases, ejecta may include lithium-based compounds. Alternatively oradditionally, ejecta may include carbon-based compounds, such as withoutlimitation carbonate esters. Ejecta may include matter in any phase orform, including solid, liquid, gas, vapor, and the like. In some cases,ejecta may undergo a phase change, for example ejecta may be vaporous asit is initially being ejected and then cool and condense into a solid orliquid after ejection. In some cases, ejecta barrier may be configuredto prevent materials ejected from a first pouch cell 404A from cominginto contact with a second pouch cell 404B. For example, in someinstances ejecta barrier 428 is substantially impermeable to ejecta frombattery pouch cell 404A-B. In some embodiments, ejecta barrier 428 mayinclude titanium. In some embodiments, ejecta barrier 428 may includecarbon fiber. In some cases, ejecta barrier 428 may include at least aone of a lithiophilic or a lithiophobic material or layer, configured toabsorb and/or repel lithium-based compounds. In some cases, ejectabarrier 428 may comprise a lithiophilic metal coating, such as silver orgold. In some cases, ejecta barrier 428 may be flexible and/or rigid. Insome cases, ejecta barrier 428 may include a sheet, a film, a foil, orthe like. For example in some cases, ejecta barrier may be between 25and 5,000 micrometers thick. In some cases, an ejecta barrier may have anominal thickness of about 2 mm. Alternatively or additionally, in somecases, an ejecta barrier may include rigid and/or structural elements,for instance which are solid. Rigid ejecta barriers 428 may includemetals, composites and the like. In some cases, ejecta barrier 428 maybe further configured to structurally support at least a pouch cell 428.For example in some cases, at least a pouch cell 428 may be mounted to arigid ejecta barrier 428.

With continued reference to FIG. 4 , battery pack 400 may additionallyinclude at least a vent 432A-B. In some cases, at least a vent 432A maybe configured to vent ejecta from first pouch cell 404A. In some cases,at least a vent 404A may be configured to vent ejecta along a flow path436A. A flow path 436A may substantially exclude second pouch cell 404B,for example fluids such as gases liquids, or any material that acts as agas or liquid, flowing along the flow path 436A may be cordoned awayfrom contact with second pouch cell 404B. For example flow path 436A maybe configured to not intersect with any surface of second pouch cell404B. Flow path 436A-B may include any channel, tube, hose, conduit, orthe like suitable for facilitating fluidic communication, for examplewith a pouch cell 404A-B. In some cases, flow path 436A-B may include acheck valve. As used in this disclosure, a “check valve” is a valve thatpermits flow of a fluid only in certain, for example one, direction. Insome cases check valve may be configured to allow flow of fluidssubstantially only away from battery pouch cell 404A-B, while preventingback flow of vented fluid to the battery pouch cell 404A-B. In somecases, check valve may include a duckbill check valve. In some cases, aduckbill check valve may have lips which are substantially in a shape ofa duckbill. Lips may be configured to open to allow forward flow (out ofthe lips), while remaining normally closed to prevent backflow (into thelips). In some cases, duckbill lips may be configured to automaticallyclose (remain normally closed), for example with use of a compliantelement, such as without limitation an elastomeric material, a spring,and the like. In some embodiments vent may include a mushroom poppetvalve. In some cases, a mushroom poppet valve may include a mushroomshaped poppet. Mushroom shaped poppet may seal against a sealingelement, for example a ring about an underside of a cap of the mushroomshaped poppet. In some cases, mushroom poppet valve may be loadedagainst sealing element, for example by way of a compliant element, suchas a spring. According to some embodiments, vent 432A-B may have avacuum applied to aid in venting of ejecta. Vacuum pressure differentialmay range from 0.1″ Hg to 46″ Hg.

With continued reference to FIG. 4 , battery pack 400 may include afirst battery pouch cell 404A and a second battery pouch cell 404B.First pouch cell 404A may include at least a first pair of electrodes408A, at least a first pair of foil tabs 412A in electricalcommunication with the first electrodes 408A, at least a first insulatorlayer 416A located substantially between the at least a first pair offoil tabs 412A, a first pouch 420A substantially encompassing the atleast a first pair of foil tabs 412A and at least a portion of the atleast a first separator layer 416A, and a first electrolyte 424A withinthe first pouch 420A. Second pouch cell 404B may include at least asecond pair of electrodes 408B, at least a second pair of foil tabs 412Bin electrical communication with the first electrodes 408B, at least asecond insulator 416B located substantially between the at least a firstpair of foil tabs 412B, a second pouch 420B substantially encompassingthe at least a second pair of foil tabs 412B and at least a portion ofthe at least a second insulator 416B, and a second electrolyte 424Bwithin the second pouch 420B. Battery pack 400 may include an ejectabarrier 428 located substantially between first pouch cell 404A andsecond pouch cell 404B. Ejecta barrier 428 may be substantiallyimpermeable to ejecta, for example ejecta from first pouch cell 404A. Insome cases, battery pack 400 may include a vent configured to ventejecta, for example from first pouch cell 404A. In some embodiments,ejecta barrier 428 may substantially encapsulates at least a portion ofpouch cell 404A-B. For example, ejecta barrier 428 may substantiallyencapsulate first pouch cell 404A. In some cases, vent may be configuredto provide fluidic communication through at least one of ejecta barrier428 and pouch 420A-B. In some cases, vent may include a seam. Seam maybe a seam of pouch 420A-B. Alternatively or additionally; seam may be aseam of ejecta barrier 428.

With continued reference to FIG. 4 , in some embodiments battery pack400 may additionally include a third pouch cell. Third pouch cell mayinclude at least a third pair of electrodes, at least a third pair offoil tabs welded to the third electrodes, at least a third insulatorlayer located substantially between the at least a third pair of foiltabs, a third pouch substantially encompassing the at least a third pairof foil tabs and the at least a third separator layer, and a thirdelectrolyte within the third pouch. Battery pack may include a pluralityincluding any number of pouch cells. In some cases, each pouch cell ofplurality of pouch cells is separated from adjacent pouch cells with atleast an ejecta barrier 428. Any pouch cell of plurality of pouch cellsin battery pack may include any component described in this disclosure,for example without limitation vents, valves, and the like.

Still referring to FIG. 4 , in some embodiments, pouch cells 404A-B mayinclude Li ion batteries which may include NCA, NMC, Lithium ironphosphate (LiFePO4) and Lithium Manganese Oxide (LMO) batteries, whichmay be mixed with another cathode chemistry to provide more specificpower if the application requires Li metal batteries, which have alithium metal anode that provides high power on demand, Li ion batteriesthat have a silicon, tin nanocrystals, graphite, graphene or titanateanode, or the like. Batteries and/or battery modules may include withoutlimitation batteries using nickel-based chemistries such as nickelcadmium or nickel metal hydride, batteries using lithium-ion batterychemistries such as a nickel cobalt aluminum (NCA), nickel manganesecobalt (NMC), lithium iron phosphate (LiFePO4), lithium cobalt oxide(LCO), and/or lithium manganese oxide (LMO), batteries using lithiumpolymer technology, metal-air batteries. Pouch cells 404A-B may includelead-based batteries such as without limitation lead acid batteries andlead carbon batteries. Pouch cells 404A-B may include lithium sulfurbatteries, magnesium ion batteries, and/or sodium ion batteries.Batteries may include solid state batteries or supercapacitors oranother suitable energy source. Batteries may be primary or secondary ora combination of both. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various devices ofcomponents that may be used as a battery module. In some cases, batterypack 400 is constructed in a manner that vents ejecta, while preventingejecta from one pouch cell from interacting with another pouch cell.

Referring now to FIG. 5 , an embodiment of sensor suite 500 ispresented. The herein disclosed system and method may comprise aplurality of sensors in the form of individual sensors or a sensor suiteworking in tandem or individually. A sensor suite may include aplurality of independent sensors, as described herein, where any numberof the described sensors may be used to detect any number of physical orelectrical quantities associated with an aircraft power system or anelectrical energy storage system. Independent sensors may includeseparate sensors measuring physical or electrical quantities that may bepowered by and/or in communication with circuits independently, whereeach may signal sensor output to a control circuit such as a usergraphical interface. In a non-limiting example, there may be fourindependent sensors housed in and/or on battery pack 104 measuringtemperature, electrical characteristic such as voltage, amperage,resistance, or impedance, or any other parameters and/or quantities asdescribed in this disclosure. In an embodiment, use of a plurality ofindependent sensors may result in redundancy configured to employ morethan one sensor that measures the same phenomenon, those sensors beingof the same type, a combination of, or another type of sensor notdisclosed, so that in the event one sensor fails, the ability of batterymanagement system 400 and/or user to detect phenomenon is maintained andin a non-limiting example, a user alter aircraft usage pursuant tosensor readings.

Sensor suite 500 may be suitable for use as first sensor suite 208and/or second sensor suite 216 hereinabove. Sensor suite 500 includes amoisture sensor 504. “Moisture”, as used in this disclosure, is thepresence of water, this may include vaporized water in air, condensationon the surfaces of objects, or concentrations of liquid water. Moisturemay include humidity. “Humidity”, as used in this disclosure, is theproperty of a gaseous medium (almost always air) to hold water in theform of vapor. An amount of water vapor contained within a parcel of aircan vary significantly. Water vapor is generally invisible to the humaneye and may be damaging to electrical components. There are threeprimary measurements of humidity, absolute, relative, specific humidity.“Absolute humidity,” for the purposes of this disclosure, describes thewater content of air and is expressed in either grams per cubic metersor grams per kilogram. “Relative humidity”, for the purposes of thisdisclosure, is expressed as a percentage, indicating a present stat ofabsolute humidity relative to a maximum humidity given the sametemperature. “Specific humidity”, for the purposes of this disclosure,is the ratio of water vapor mass to total moist air parcel mass, whereparcel is a given portion of a gaseous medium. Moisture sensor 504 maybe psychrometer. Moisture sensor 504 may be a hygrometer. Moisturesensor 504 may be configured to act as or include a humidistat. A“humidistat”, for the purposes of this disclosure, is ahumidity-triggered switch, often used to control another electronicdevice. Moisture sensor 504 may use capacitance to measure relativehumidity and include in itself, or as an external component, include adevice to convert relative humidity measurements to absolute humiditymeasurements. “Capacitance”, for the purposes of this disclosure, is theability of a system to store an electric charge, in this case the systemis a parcel of air which may be near, adjacent to, or above a batterycell.

With continued reference to FIG. 5 , sensor suite 500 may includeelectrical sensors 508. Electrical sensors 508 may be configured tomeasure voltage across a component, electrical current through acomponent, and resistance of a component. Electrical sensors 508 mayinclude separate sensors to measure each of the previously disclosedelectrical characteristics such as voltmeter, ammeter, and ohmmeter,respectively.

Alternatively or additionally, and with continued reference to FIG. 5 ,sensor suite 500 include a sensor or plurality thereof that may detectvoltage and direct the charging of individual battery cells according tocharge level; detection may be performed using any suitable component,set of components, and/or mechanism for direct or indirect measurementand/or detection of voltage levels, including without limitationcomparators, analog to digital converters, any form of voltmeter, or thelike. Sensor suite 500 and/or a control circuit incorporated thereinand/or communicatively connected thereto may be configured to adjustcharge to one or more battery cells as a function of a charge leveland/or a detected parameter. For instance, and without limitation,sensor suite 500 may be configured to determine that a charge level of abattery cell is high based on a detected voltage level of that batterycell or portion of the battery pack. Sensor suite 500 may alternativelyor additionally detect a charge reduction event, defined for purposes ofthis disclosure as any temporary or permanent state of a battery cellrequiring reduction or cessation of charging; a charge reduction eventmay include a cell being fully charged and/or a cell undergoing aphysical and/or electrical process that makes continued charging at acurrent voltage and/or current level inadvisable due to a risk that thecell will be damaged, will overheat, or the like. Detection of a chargereduction event may include detection of a temperature, of the cellabove a threshold level, detection of a voltage and/or resistance levelabove or below a threshold, or the like. Sensor suite 500 may includedigital sensors, analog sensors, or a combination thereof. Sensor suite500 may include digital-to-analog converters (DAC), analog-to-digitalconverters (ADC, A/D, A-to-D), a combination thereof, or other signalconditioning components used in transmission of a first plurality ofbattery pack data 428 to a destination over wireless or wiredconnection.

With continued reference to FIG. 5 , sensor suite 500 may includethermocouples, thermistors, thermometers, passive infrared sensors,resistance temperature sensors (RTD's), semiconductor based integratedcircuits (IC), a combination thereof or another undisclosed sensor type,alone or in combination. Temperature, for the purposes of thisdisclosure, and as would be appreciated by someone of ordinary skill inthe art, is a measure of the heat energy of a system. Temperature, asmeasured by any number or combinations of sensors present within sensorsuite 500, may be measured in Fahrenheit (° F.), Celsius (° C.), Kelvin(° K), or another scale alone or in combination. The temperaturemeasured by sensors may comprise electrical signals which aretransmitted to their appropriate destination wireless or through a wiredconnection.

With continued reference to FIG. 5 , sensor suite 500 may include asensor configured to detect gas that may be emitted during or after acell failure. “Cell failure”, for the purposes of this disclosure,refers to a malfunction of a battery cell, which may be anelectrochemical cell, that renders the cell inoperable for its designedfunction, namely providing electrical energy to at least a portion of anelectric aircraft. Byproducts of cell failure 512 may include gaseousdischarge including oxygen, hydrogen, carbon dioxide, methane, carbonmonoxide, a combination thereof, or another undisclosed gas, alone or incombination. Further the sensor configured to detect vent gas fromelectrochemical cells may comprise a gas detector. For the purposes ofthis disclosure, a “gas detector” is a device used to detect a gas ispresent in an area. Gas detectors, and more specifically, the gas sensorthat may be used in sensor suite 500, may be configured to detectcombustible, flammable, toxic, oxygen depleted, a combination thereof,or another type of gas alone or in combination. The gas sensor that maybe present in sensor suite 500 may include a combustible gas,photoionization detectors, electrochemical gas sensors, ultrasonicsensors, metal-oxide-semiconductor (MOS) sensors, infrared imagingsensors, a combination thereof, or another undisclosed type of gassensor alone or in combination. Sensor suite 500 may include sensorsthat are configured to detect non-gaseous byproducts of cell failure 512including, in non-limiting examples, liquid chemical leaks includingaqueous alkaline solution, ionomer, molten phosphoric acid, liquidelectrolytes with redox shuttle and ionomer, and salt water, amongothers. Sensor suite 500 may include sensors that are configured todetect non-gaseous byproducts of cell failure 512 including, innon-limiting examples, electrical anomalies as detected by any of theprevious disclosed sensors or components.

With continued reference to FIG. 5 , sensor suite 500 may be configuredto detect events where voltage nears an upper voltage threshold or lowervoltage threshold. The upper voltage threshold may be stored in batterydatabase 160 for comparison with an instant measurement taken by anycombination of sensors present within sensor suite 500. The uppervoltage threshold may be calculated and calibrated based on factorsrelating to battery cell health, maintenance history, location withinbattery pack, designed application, and type, among others. Sensor suite500 may measure voltage at an instant, over a period of time, orperiodically. Sensor suite 500 may be configured to operate at any ofthese detection modes, switch between modes, or simultaneous measure inmore than one mode. First battery management component 404 may detectthrough sensor suite 500 events where voltage nears the lower voltagethreshold. The lower voltage threshold may indicate power loss to orfrom an individual battery cell or portion of the battery pack. Firstbattery management component 404 may detect through sensor suite 500events where voltage exceeds the upper and lower voltage threshold.Events where voltage exceeds the upper and lower voltage threshold mayindicate battery cell failure or electrical anomalies that could lead topotentially dangerous situations for aircraft and personnel that may bepresent in or near its operation.

Now referring to FIG. 6 , a flow diagram of an exemplary method 600 forpredicting degradation of a battery for use in an electric vehicle isprovided. Method 600 may include a method for predicting degradation ofa battery for use in any electric vehicle such as an electric aircraft.Method 600, at step 605, includes detecting, by at least a pack monitorunit (PMU), a battery pack datum of a battery pack. In a non-limitingembodiment, the at least a pack monitor unit may include a first PMU anda second PMU. The at least a PMU may include any sensor as described inthe entirety of this disclosure. The battery pack datum may include anybattery pack datum as described herein. In a non-limiting embodiment,method 600, at step 605, may include detecting by the first PMU a firstbattery pack datum. The first battery pack datum may include any firstbattery pack datum as described herein. In a non-limiting embodiment,method 600, at step 605, may include detecting by the second PMU asecond battery pack datum. The second battery pack datum may include anysecond battery pack datum as described herein. In a non-limitingembodiment, detecting the first battery pack datum and second batterypack datum may include detecting data from the same battery pack inwhich the first battery pack datum and second battery pack datum are thesame. In a non-limiting embodiment, method 600, at step 605, includechecking for any battery abnormalities as an initial screening beforedetermining a battery degradation prediction by a computing device. In anon-limiting embodiment, detecting the battery pack datum may includedetecting a state of charge of the battery pack. The state of charge ofthe battery pack may include any state of charge of the battery packdescribed herein. In a non-limiting embodiment, detecting the batterypack datum may include detecting a state of health of the battery pack.The state of health of the battery pack may include any state of healthof the battery pack described herein. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of the variousmethods and types of data in the detecting of battery data for purposesas described herein.

With continued reference to FIG. 6 , method 600, may includecommunicatively connecting a computing device to a network. Thecomputing device may include a flight controller. The computing devicemay include any computing device as described herein. The flightcontroller may include any flight controller as described herein. Thenetwork may include any network as described herein. In a non-limitingembodiment, the network may include a mesh network, such as an avionicmesh network. The avionic mesh network may include any avionic meshnetwork as described herein. In a non-limiting embodiment, method 600,may include authenticating the computing device before connecting thenetwork. For example and without limitation, the computing device may bedisposed onto the electric aircraft wherein the electric aircraft mayattempt to connect to the network to transmit any data detected forcalculation or manipulation. In a non-limiting embodiment, method 600may include connecting to as a function of at least a physical CAN busunit. The at least a physical CAN bus unit may include any physical CANbus unit as described herein. For example and without limitation, thebattery pack may be connected to the computing device by any voltagering bus, wherein the bus element may include a first physical CAN busunit and a second physical CAN bus unit in which the first PMU may beconfigured to transmit the first battery pack datum to the computingdevice via the first physical CAN bus unit and the second PMU may beconfigured to transmit the second battery pack datum to the computingdevice via the second physical CAN bus unit.

With continued reference to FIG. 6 , method 600, at step 710, includesreceiving, by the computing device, the battery pack datum from the atleast a pack monitor unit. In a non-limiting embodiment, method 600, atstep 710, may include receiving the battery pack datum as a function ofthe connection to the network. Receiving the battery pack datum mayinclude a remote device receiving the battery pack datum for anycomputation of data once a connection with the network is established.

With continued reference to FIG. 6 , method 600, at step 715, includesgenerating, by a simulator machine, a battery pack model associated withthe battery pack of the electric vehicle as a function of the batterypack datum. The simulator machine may include any simulator machine asdescribed herein. The battery pack model may include any battery packmodel as described herein. In a non-limiting embodiment, method 600, atstep 615, may include simulating, by the simulator machine, a virtualrepresentation of a digital twin, wherein the digital twin furthercomprises the battery pack model. The virtual representation may includeany virtual representation as described herein. The digital twin mayinclude any digital twin as described herein. In a non-limitingembodiment, method 600 may be used in a simulated environment of asimulated electric aircraft using a simulator module. The simulatormodule may include any simulator module as described herein. In anon-limiting embodiment, method 600, at step 615, may include simulatinga plurality of virtual instances of the battery pack datum updatedcontinuously. The plurality of virtual instances may include anyplurality of virtual instances as described herein.

With continued reference to FIG. 6 , method 600, at step 620, includesdetermining, by the computing device, a battery degradation predictionas a function of the battery pack datum and the battery pack model. Thebattery degradation prediction may include any battery degradationprediction as described herein. In a non-limiting embodiment, method 600may include generating a machine-learning model, wherein themachine-learning model is configured to receive the battery pack datumas an input and output a battery degradation model as a function of adegradation training set. The battery degradation model may include anybattery degradation model as described herein. The machine-learningmodel may include any machine-learning model as described herein. Thedegradation training set may include any degradation training set. In anon-limiting embodiment, generating the battery degradation predictionmay include generating the battery degradation prediction as a functionof a plurality of battery pack models. In a non-limiting embodiment,method 600 may include storing the battery pack datum in a batterydatabase and storing the battery pack model in the battery database. Thebattery database may include any battery database as described herein.For example and without limitation, method 600, at step 620, may includeretrieving data from the battery database as a training set for themachine-learning model. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of the various methods andembodiments for simulation for purposes as described herein.

Referring now to FIG. 7 , an exemplary embodiment of an electricaircraft 700, which may include, or be incorporated with, a system foroptimization of a recharging flight plan is illustrated. As used in thisdisclosure an “aircraft” is any vehicle that may fly by gaining supportfrom the air. As a non-limiting example, aircraft may include airplanes,helicopters, commercial and/or recreational aircrafts, instrument flightaircrafts, drones, electric aircrafts, airliners, rotorcrafts, verticaltakeoff and landing aircrafts, jets, airships, blimps, gliders,paramotors, and the like thereof.

Still referring to FIG. 7 , aircraft 700 may include an electricallypowered aircraft. In embodiments, electrically powered aircraft may bean electric vertical takeoff and landing (eVTOL) aircraft. Aircraft 700may include an unmanned aerial vehicle and/or a drone. Electric aircraftmay be capable of rotor-based cruising flight, rotor-based takeoff,rotor-based landing, fixed-wing cruising flight, airplane-style takeoff,airplane-style landing, and/or any combination thereof. Electricaircraft may include one or more manned and/or unmanned aircrafts.Electric aircraft may include one or more all-electric short takeoff andlanding (eSTOL) aircrafts. For example, and without limitation, eSTOLaircrafts may accelerate the plane to a flight speed on takeoff anddecelerate the plane after landing. In an embodiment, and withoutlimitation, electric aircraft may be configured with an electricpropulsion assembly. For purposes of description herein, the terms“upper”, “lower”, “left”, “rear”, “right”, “front”, “vertical”,“horizontal”, “upward”, “downward”, “forward”, “backward” andderivatives thereof shall relate to the invention as oriented in FIG. 7.

Still referring to FIG. 7 , aircraft 700 includes a fuselage 704. Asused in this disclosure a “fuselage” is the main body of an aircraft, orin other words, the entirety of the aircraft except for the cockpit,nose, wings, empennage, nacelles, any and all control surfaces, andgenerally contains an aircraft's payload. Fuselage 704 may includestructural elements that physically support a shape and structure of anaircraft. Structural elements may take a plurality of forms, alone or incombination with other types. Structural elements may vary depending ona construction type of aircraft such as without limitation a fuselage704. Fuselage 704 may comprise a truss structure. A truss structure maybe used with a lightweight aircraft and comprises welded steel tubetrusses. A “truss,” as used in this disclosure, is an assembly of beamsthat create a rigid structure, often in combinations of triangles tocreate three-dimensional shapes. A truss structure may alternativelycomprise wood construction in place of steel tubes, or a combinationthereof. In embodiments, structural elements may comprise steel tubesand/or wood beams. In an embodiment, and without limitation, structuralelements may include an aircraft skin. Aircraft skin may be layered overthe body shape constructed by trusses. Aircraft skin may comprise aplurality of materials such as plywood sheets, aluminum, fiberglass,and/or carbon fiber, the latter of which will be addressed in greaterdetail later herein.

In embodiments, and with continued reference to FIG. 7 , aircraftfuselage 704 may include and/or be constructed using geodesicconstruction. Geodesic structural elements may include stringers woundabout formers (which may be alternatively called station frames) inopposing spiral directions. A “stringer,” as used in this disclosure, isa general structural element that includes a long, thin, and rigid stripof metal or wood that is mechanically coupled to and spans a distancefrom, station frame to station frame to create an internal skeleton onwhich to mechanically couple aircraft skin. A former (or station frame)may include a rigid structural element that is disposed along a lengthof an interior of aircraft fuselage 704 orthogonal to a longitudinal(nose to tail) axis of the aircraft and may form a general shape offuselage 704. A former may include differing cross-sectional shapes atdiffering locations along fuselage 704, as the former is the structuralelement that informs the overall shape of a fuselage 704 curvature. Inembodiments, aircraft skin may be anchored to formers and strings suchthat the outer mold line of a volume encapsulated by formers andstringers comprises the same shape as aircraft 700 when installed. Inother words, former(s) may form a fuselage's ribs, and the stringers mayform the interstitials between such ribs. The spiral orientation ofstringers about formers may provide uniform robustness at any point onan aircraft fuselage such that if a portion sustains damage, anotherportion may remain largely unaffected. Aircraft skin may be mechanicallycoupled to underlying stringers and formers and may interact with afluid, such as air, to generate lift and perform maneuvers.

In an embodiment, and still referring to FIG. 7 , fuselage 704 mayinclude and/or be constructed using monocoque construction. Monocoqueconstruction may include a primary structure that forms a shell (or skinin an aircraft's case) and supports physical loads. Monocoque fuselagesare fuselages in which the aircraft skin or shell is also the primarystructure. In monocoque construction aircraft skin would support tensileand compressive loads within itself and true monocoque aircraft can befurther characterized by the absence of internal structural elements.Aircraft skin in this construction method is rigid and can sustain itsshape with no structural assistance form underlying skeleton-likeelements. Monocoque fuselage may comprise aircraft skin made fromplywood layered in varying grain directions, epoxy-impregnatedfiberglass, carbon fiber, or any combination thereof.

According to embodiments, and further referring to FIG. 7 , fuselage 704may include a semi-monocoque construction. Semi-monocoque construction,as used herein, is a partial monocoque construction, wherein a monocoqueconstruction is describe above detail. In semi-monocoque construction,aircraft fuselage 704 may derive some structural support from stressedaircraft skin and some structural support from underlying framestructure made of structural elements. Formers or station frames can beseen running transverse to the long axis of fuselage 704 with circularcutouts which are generally used in real-world manufacturing for weightsavings and for the routing of electrical harnesses and other modernon-board systems. In a semi-monocoque construction, stringers are thin,long strips of material that run parallel to fuselage's long axis.Stringers may be mechanically coupled to formers permanently, such aswith rivets. Aircraft skin may be mechanically coupled to stringers andformers permanently, such as by rivets as well. A person of ordinaryskill in the art will appreciate, upon reviewing the entirety of thisdisclosure, that there are numerous methods for mechanical fastening ofthe aforementioned components like screws, nails, dowels, pins, anchors,adhesives like glue or epoxy, or bolts and nuts, to name a few. A subsetof fuselage under the umbrella of semi-monocoque construction includesunibody vehicles. Unibody, which is short for “unitized body” oralternatively “unitary construction”, vehicles are characterized by aconstruction in which the body, floor plan, and chassis form a singlestructure. In the aircraft world, unibody may be characterized byinternal structural elements like formers and stringers beingconstructed in one piece, integral to the aircraft skin as well as anyfloor construction like a deck.

Still referring to FIG. 7 , stringers and formers, which may account forthe bulk of an aircraft structure excluding monocoque construction, maybe arranged in a plurality of orientations depending on aircraftoperation and materials. Stringers may be arranged to carry axial(tensile or compressive), shear, bending or torsion forces throughouttheir overall structure. Due to their coupling to aircraft skin,aerodynamic forces exerted on aircraft skin will be transferred tostringers. A location of said stringers greatly informs the type offorces and loads applied to each and every stringer, all of which may behandled by material selection, cross-sectional area, and mechanicalcoupling methods of each member. A similar assessment may be made forformers. In general, formers may be significantly larger incross-sectional area and thickness, depending on location, thanstringers. Both stringers and formers may comprise aluminum, aluminumalloys, graphite epoxy composite, steel alloys, titanium, or anundisclosed material alone or in combination.

In an embodiment, and still referring to FIG. 7 , stressed skin, whenused in semi-monocoque construction is the concept where the skin of anaircraft bears partial, yet significant, load in an overall structuralhierarchy. In other words, an internal structure, whether it be a frameof welded tubes, formers and stringers, or some combination, may not besufficiently strong enough by design to bear all loads. The concept ofstressed skin may be applied in monocoque and semi-monocoqueconstruction methods of fuselage 704. Monocoque comprises onlystructural skin, and in that sense, aircraft skin undergoes stress byapplied aerodynamic fluids imparted by the fluid. Stress as used incontinuum mechanics may be described in pound-force per square inch(lbf/in²) or Pascals (Pa). In semi-monocoque construction stressed skinmay bear part of aerodynamic loads and additionally may impart force onan underlying structure of stringers and formers.

Still referring to FIG. 7 , it should be noted that an illustrativeembodiment is presented only, and this disclosure in no way limits theform or construction method of a system and method for loading payloadinto an eVTOL aircraft. In embodiments, fuselage 704 may be configurablebased on the needs of the eVTOL per specific mission or objective. Thegeneral arrangement of components, structural elements, and hardwareassociated with storing and/or moving a payload may be added or removedfrom fuselage 704 as needed, whether it is stowed manually, automatedly,or removed by personnel altogether. Fuselage 704 may be configurable fora plurality of storage options. Bulkheads and dividers may be installedand uninstalled as needed, as well as longitudinal dividers wherenecessary. Bulkheads and dividers may be installed using integratedslots and hooks, tabs, boss and channel, or hardware like bolts, nuts,screws, nails, clips, pins, and/or dowels, to name a few. Fuselage 704may also be configurable to accept certain specific cargo containers, ora receptable that can, in turn, accept certain cargo containers.

Still referring to FIG. 7 , aircraft 700 may include a plurality oflaterally extending elements attached to fuselage 704. As used in thisdisclosure a “laterally extending element” is an element that projectsessentially horizontally from fuselage, including an outrigger, a spar,and/or a fixed wing that extends from fuselage. Wings may be structureswhich include airfoils configured to create a pressure differentialresulting in lift. Wings may generally dispose on the left and rightsides of the aircraft symmetrically, at a point between nose andempennage. Wings may comprise a plurality of geometries in planformview, swept swing, tapered, variable wing, triangular, oblong,elliptical, square, among others. A wing's cross section geometry maycomprise an airfoil. An “airfoil” as used in this disclosure is a shapespecifically designed such that a fluid flowing above and below it exertdiffering levels of pressure against the top and bottom surface. Inembodiments, the bottom surface of an aircraft can be configured togenerate a greater pressure than does the top, resulting in lift.Laterally extending element may comprise differing and/or similarcross-sectional geometries over its cord length or the length from wingtip to where wing meets the aircraft's body. One or more wings may besymmetrical about the aircraft's longitudinal plane, which comprises thelongitudinal or roll axis reaching down the center of the aircraftthrough the nose and empennage, and the plane's yaw axis. Laterallyextending element may comprise controls surfaces configured to becommanded by a pilot or pilots to change a wing's geometry and thereforeits interaction with a fluid medium, like air. Control surfaces maycomprise flaps, ailerons, tabs, spoilers, and slats, among others. Thecontrol surfaces may dispose on the wings in a plurality of locationsand arrangements and in embodiments may be disposed at the leading andtrailing edges of the wings, and may be configured to deflect up, down,forward, aft, or a combination thereof. An aircraft, including adual-mode aircraft may comprise a combination of control surfaces toperform maneuvers while flying or on ground.

Still referring to FIG. 7 , aircraft 700 includes a plurality of flightcomponents 708. As used in this disclosure a “flight component” is acomponent that promotes flight and guidance of an aircraft. In anembodiment, flight component 708 may be mechanically coupled to anaircraft. As used herein, a person of ordinary skill in the art wouldunderstand “mechanically coupled” to mean that at least a portion of adevice, component, or circuit is connected to at least a portion of theaircraft via a mechanical coupling. Said mechanical coupling caninclude, for example, rigid coupling, such as beam coupling, bellowscoupling, bushed pin coupling, constant velocity, split-muff coupling,diaphragm coupling, disc coupling, donut coupling, elastic coupling,flexible coupling, fluid coupling, gear coupling, grid coupling, hirthjoints, hydrodynamic coupling, jaw coupling, magnetic coupling, Oldhamcoupling, sleeve coupling, tapered shaft lock, twin spring coupling, ragjoint coupling, universal joints, or any combination thereof. In anembodiment, mechanical coupling may be used to connect the ends ofadjacent parts and/or objects of an electric aircraft. Further, in anembodiment, mechanical coupling may be used to join two pieces ofrotating electric aircraft components.

Still referring to FIG. 7 , plurality of flight components 708 mayinclude at least a lift propulsor component 712. As used in thisdisclosure a “lift propulsor component” is a component and/or deviceused to propel a craft upward by exerting downward force on a fluidmedium, which may include a gaseous medium such as air or a liquidmedium such as water. Lift propulsor component 712 may include anydevice or component that consumes electrical power on demand to propelan electric aircraft in a direction or other vehicle while on ground orin-flight. For example, and without limitation, lift propulsor component712 may include a rotor, propeller, paddle wheel and the like thereof,wherein a rotor is a component that produces torque along thelongitudinal axis, and a propeller produces torquer along the verticalaxis. In an embodiment, lift propulsor component 712 includes aplurality of blades. As used in this disclosure a “blade” is a propellerthat converts rotary motion from an engine or other power source into aswirling slipstream. In an embodiment, blade may convert rotary motionto push the propeller forwards or backwards. In an embodiment liftpropulsor component 712 may include a rotating power-driven hub, towhich are attached several radial airfoil-section blades such that thewhole assembly rotates about a longitudinal axis. Blades may beconfigured at an angle of attack, wherein an angle of attack isdescribed in detail below. In an embodiment, and without limitation,angle of attack may include a fixed angle of attack. As used in thisdisclosure a “fixed angle of attack” is fixed angle between a chord lineof a blade and relative wind. As used in this disclosure a “fixed angle”is an angle that is secured and/or unmovable from the attachment point.For example, and without limitation fixed angle of attack may be 3.2° asa function of a pitch angle of 19.7° and a relative wind angle 16.5°. Inanother embodiment, and without limitation, angle of attack may includea variable angle of attack. As used in this disclosure a “variable angleof attack” is a variable and/or moveable angle between a chord line of ablade and relative wind. As used in this disclosure a “variable angle”is an angle that is moveable from an attachment point. For example, andwithout limitation variable angle of attack may be a first angle of 7.7°as a function of a pitch angle of 17.1° and a relative wind angle 16.4°,wherein the angle adjusts and/or shifts to a second angle of 16.7° as afunction of a pitch angle of 16.1° and a relative wind angle 16.4°. Inan embodiment, angle of attack be configured to produce a fixed pitchangle. As used in this disclosure a “fixed pitch angle” is a fixed anglebetween a cord line of a blade and the rotational velocity direction.For example, and without limitation, fixed pitch angle may include 18°.In another embodiment fixed angle of attack may be manually variable toa few set positions to adjust one or more lifts of the aircraft prior toflight. In an embodiment, blades for an aircraft are designed to befixed to their hub at an angle similar to the thread on a screw makes anangle to the shaft; this angle may be referred to as a pitch or pitchangle which will determine a speed of forward movement as the bladerotates.

In an embodiment, and still referring to FIG. 7 , lift propulsorcomponent 712 may be configured to produce a lift. As used in thisdisclosure a “lift” is a perpendicular force to the oncoming flowdirection of fluid surrounding the surface. For example, and withoutlimitation relative air speed may be horizontal to aircraft 700, whereinlift force may be a force exerted in a vertical direction, directingaircraft 700 upwards. In an embodiment, and without limitation, liftpropulsor component 712 may produce lift as a function of applying atorque to lift propulsor component. As used in this disclosure a“torque” is a measure of force that causes an object to rotate about anaxis in a direction. For example, and without limitation, torque mayrotate an aileron and/or rudder to generate a force that may adjustand/or affect altitude, airspeed velocity, groundspeed velocity,direction during flight, and/or thrust. For example, one or more flightcomponents such as a power sources may apply a torque on lift propulsorcomponent 712 to produce lift. As used in this disclosure a “powersource” is a source that that drives and/or controls any other flightcomponent. For example, and without limitation power source may includea motor that operates to move one or more lift propulsor components, todrive one or more blades, or the like thereof. A motor may be driven bydirect current (DC) electric power and may include, without limitation,brushless DC electric motors, switched reluctance motors, inductionmotors, or any combination thereof. A motor may also include electronicspeed controllers or other components for regulating motor speed,rotation direction, and/or dynamic braking.

Still referring to FIG. 7 , power source may include an energy source.An energy source may include, for example, an electrical energy source agenerator, a photovoltaic device, a fuel cell such as a hydrogen fuelcell, direct methanol fuel cell, and/or solid oxide fuel cell, anelectric energy storage device (e.g., a capacitor, an inductor, and/or abattery). An electrical energy source may also include a battery cell,or a plurality of battery cells connected in series into a module andeach module connected in series or in parallel with other modules.Configuration of an energy source containing connected modules may bedesigned to meet an energy or power requirement and may be designed tofit within a designated footprint in an electric aircraft in whichaircraft 700 may be incorporated.

In an embodiment, and still referring to FIG. 7 , an energy source maybe used to provide a steady supply of electrical power to a load overthe course of a flight by a vehicle or other electric aircraft. Forexample, an energy source may be capable of providing sufficient powerfor “cruising” and other relatively low-energy phases of flight. Anenergy source may also be capable of providing electrical power for somehigher-power phases of flight as well, particularly when the energysource is at a high SOC, as may be the case for instance during takeoffIn an embodiment, an energy source may be capable of providingsufficient electrical power for auxiliary loads including withoutlimitation, lighting, navigation, communications, de-icing, steering orother systems requiring power or energy. Further, an energy source maybe capable of providing sufficient power for controlled descent andlanding protocols, including, without limitation, hovering descent orrunway landing. As used herein an energy source may have high powerdensity where electrical power an energy source can usefully produce perunit of volume and/or mass is relatively high. “Electrical power,” asused in this disclosure, is defined as a rate of electrical energy perunit time. An energy source may include a device for which power thatmay be produced per unit of volume and/or mass has been optimized, atthe expense of the maximal total specific energy density or powercapacity, during design. Non-limiting examples of items that may be usedas at least an energy source may include batteries used for startingapplications including Li ion batteries which may include NCA, NMC,Lithium iron phosphate (LiFePO4) and Lithium Manganese Oxide (LMO)batteries, which may be mixed with another cathode chemistry to providemore specific power if the application requires Li metal batteries,which have a lithium metal anode that provides high power on demand, Liion batteries that have a silicon or titanite anode, energy source maybe used, in an embodiment, to provide electrical power to an electricaircraft or drone, such as an electric aircraft vehicle, during momentsrequiring high rates of power output, including without limitationtakeoff, landing, thermal de-icing and situations requiring greaterpower output for reasons of stability, such as high turbulencesituations, as described in further detail below. A battery may include,without limitation a battery using nickel based chemistries such asnickel cadmium or nickel metal hydride, a battery using lithium ionbattery chemistries such as a nickel cobalt aluminum (NCA), nickelmanganese cobalt (NMC), lithium iron phosphate (LiFePO4), lithium cobaltoxide (LCO), and/or lithium manganese oxide (LMO), a battery usinglithium polymer technology, lead-based batteries such as withoutlimitation lead acid batteries, metal-air batteries, or any othersuitable battery. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various devices ofcomponents that may be used as an energy source.

Still referring to FIG. 7 , an energy source may include a plurality ofenergy sources, referred to herein as a module of energy sources. Amodule may include batteries connected in parallel or in series or aplurality of modules connected either in series or in parallel designedto deliver both the power and energy requirements of the application.Connecting batteries in series may increase the voltage of at least anenergy source which may provide more power on demand. High voltagebatteries may require cell matching when high peak load is needed. Asmore cells are connected in strings, there may exist the possibility ofone cell failing which may increase resistance in the module and reducean overall power output as a voltage of the module may decrease as aresult of that failing cell. Connecting batteries in parallel mayincrease total current capacity by decreasing total resistance, and italso may increase overall amp-hour capacity. Overall energy and poweroutputs of at least an energy source may be based on individual batterycell performance or an extrapolation based on measurement of at least anelectrical parameter. In an embodiment where an energy source includes aplurality of battery cells, overall power output capacity may bedependent on electrical parameters of each individual cell. If one cellexperiences high self-discharge during demand, power drawn from at leastan energy source may be decreased to avoid damage to the weakest cell.An energy source may further include, without limitation, wiring,conduit, housing, cooling system and battery management system. Personsskilled in the art will be aware, after reviewing the entirety of thisdisclosure, of many different components of an energy source.

In an embodiment and still referring to FIG. 7 , plurality of flightcomponents 708 may be arranged in a quad copter orientation. As used inthis disclosure a “quad copter orientation” is at least a lift propulsorcomponent oriented in a geometric shape and/or pattern, wherein each ofthe lift propulsor components are located along a vertex of thegeometric shape. For example, and without limitation, a square quadcopter orientation may have four lift propulsor components oriented inthe geometric shape of a square, wherein each of the four lift propulsorcomponents are located along the four vertices of the square shape. As afurther non-limiting example, a hexagonal quad copter orientation mayhave six lift propulsor components oriented in the geometric shape of ahexagon, wherein each of the six lift propulsor components are locatedalong the six vertices of the hexagon shape. In an embodiment, andwithout limitation, quad copter orientation may include a first set oflift propulsor components and a second set of lift propulsor components,wherein the first set of lift propulsor components and the second set oflift propulsor components may include two lift propulsor componentseach, wherein the first set of lift propulsor components and a secondset of lift propulsor components are distinct from one another. Forexample, and without limitation, the first set of lift propulsorcomponents may include two lift propulsor components that rotate in aclockwise direction, wherein the second set of lift propulsor componentsmay include two lift propulsor components that rotate in acounterclockwise direction. In an embodiment, and without limitation,the first set of propulsor lift components may be oriented along a lineoriented 75° from the longitudinal axis of aircraft 700. In anotherembodiment, and without limitation, the second set of propulsor liftcomponents may be oriented along a line oriented 135° from thelongitudinal axis, wherein the first set of lift propulsor componentsline and the second set of lift propulsor components are perpendicularto each other.

Still referring to FIG. 7 , plurality of flight components 708 mayinclude a pusher component 716. As used in this disclosure a “pushercomponent” is a component that pushes and/or thrusts an aircraft througha medium. As a non-limiting example, pusher component 716 may include apusher propeller, a paddle wheel, a pusher motor, a pusher propulsor,and the like.

Additionally, or alternatively, pusher flight component may include aplurality of pusher flight components. Pusher component 716 isconfigured to produce a forward thrust. As used in this disclosure a“forward thrust” is a thrust that forces aircraft through a medium in ahorizontal direction, wherein a horizontal direction is a directionparallel to the longitudinal axis. As a non-limiting example, forwardthrust may include a force of 1145 N to force aircraft to in ahorizontal direction along the longitudinal axis. As a furthernon-limiting example, forward thrust may include a force of, as anon-limiting example, 300 N to force aircraft 700 in a horizontaldirection along a longitudinal axis. As a further non-limiting example,pusher component 716 may twist and/or rotate to pull air behind it and,at the same time, push aircraft 700 forward with an equal amount offorce. In an embodiment, and without limitation, the more air forcedbehind aircraft, the greater the thrust force with which the aircraft ispushed horizontally will be. In another embodiment, and withoutlimitation, forward thrust may force aircraft 700 through the medium ofrelative air. Additionally or alternatively, plurality of flightcomponents 708 may include one or more puller components. As used inthis disclosure a “puller component” is a component that pulls and/ortows an aircraft through a medium. As a non-limiting example, pullercomponent may include a flight component such as a puller propeller, apuller motor, a tractor propeller, a puller propulsor, and the like.Additionally, or alternatively, puller component may include a pluralityof puller flight components.

In an embodiment and still referring to FIG. 7 , aircraft 700 mayinclude a flight controller located within fuselage 704, wherein aflight controller is described in detail below, in reference to FIG. 7 .In an embodiment, and without limitation, flight controller may beconfigured to operate a fixed-wing flight capability. As used in thisdisclosure a “fixed-wing flight capability” is a method of flightwherein the plurality of laterally extending elements generate lift. Forexample, and without limitation, fixed-wing flight capability maygenerate lift as a function of an airspeed of aircraft 70 and one ormore airfoil shapes of the laterally extending elements, wherein anairfoil is described above in detail. As a further non-limiting example,flight controller may operate the fixed-wing flight capability as afunction of reducing applied torque on lift propulsor component 712. Forexample, and without limitation, flight controller may reduce a torqueof 19 Nm applied to a first set of lift propulsor components to a torqueof 16 Nm. As a further non-limiting example, flight controller mayreduce a torque of 12 Nm applied to a first set of lift propulsorcomponents to a torque of 0 Nm. In an embodiment, and withoutlimitation, flight controller may produce fixed-wing flight capabilityas a function of increasing forward thrust exerted by pusher component716. For example, and without limitation, flight controller may increasea forward thrust of 700 kN produced by pusher component 716 to a forwardthrust of 1669 kN. In an embodiment, and without limitation, an amountof lift generation may be related to an amount of forward thrustgenerated to increase airspeed velocity, wherein the amount of liftgeneration may be directly proportional to the amount of forward thrustproduced. Additionally or alternatively, flight controller may includean inertia compensator. As used in this disclosure an “inertiacompensator” is one or more computing devices, electrical components,logic circuits, processors, and the like there of that are configured tocompensate for inertia in one or more lift propulsor components presentin aircraft 700. Inertia compensator may alternatively or additionallyinclude any computing device used as an inertia compensator as describedin U.S. Nonprovisional application Ser. No. 17/106,557, and entitled“SYSTEM AND METHOD FOR FLIGHT CONTROL IN ELECTRIC AIRCRAFT,” theentirety of which is incorporated herein by reference.

In an embodiment, and still referring to FIG. 7 , flight controller maybe configured to perform a reverse thrust command. As used in thisdisclosure a “reverse thrust command” is a command to perform a thrustthat forces a medium towards the relative air opposing aircraft 190. Forexample, reverse thrust command may include a thrust of 180 N directedtowards the nose of aircraft to at least repel and/or oppose therelative air. Reverse thrust command may alternatively or additionallyinclude any reverse thrust command as described in U.S. Nonprovisionalapplication Ser. No. 17/319,155 and entitled “AIRCRAFT HAVING REVERSETHRUST CAPABILITIES,” the entirety of which is incorporated herein byreference. In another embodiment, flight controller may be configured toperform a regenerative drag operation. As used in this disclosure a“regenerative drag operation” is an operating condition of an aircraft,wherein the aircraft has a negative thrust and/or is reducing inairspeed velocity. For example, and without limitation, regenerativedrag operation may include a positive propeller speed and a negativepropeller thrust. Regenerative drag operation may alternatively oradditionally include any regenerative drag operation as described inU.S. Nonprovisional application Ser. No. 17/319,155.

In an embodiment, and still referring to FIG. 7 , flight controller maybe configured to perform a corrective action as a function of a failureevent. As used in this disclosure a “corrective action” is an actionconducted by the plurality of flight components to correct and/or altera movement of an aircraft. For example, and without limitation, acorrective action may include an action to reduce a yaw torque generatedby a failure event. Additionally or alternatively, corrective action mayinclude any corrective action as described in U.S. Nonprovisionalapplication Ser. No. 17/222,539, and entitled “AIRCRAFT FORSELF-NEUTRALIZING FLIGHT,” the entirety of which is incorporated hereinby reference. As used in this disclosure a “failure event” is a failureof a lift propulsor component of the plurality of lift propulsorcomponents. For example, and without limitation, a failure event maydenote a rotation degradation of a rotor, a reduced torque of a rotor,and the like thereof.

Now referring to FIG. 8 , an exemplary embodiment 800 of a flightcontroller 804 is illustrated. As used in this disclosure a “flightcontroller” is a computing device of a plurality of computing devicesdedicated to data storage, security, distribution of traffic for loadbalancing, and flight instruction. Flight controller 804 may includeand/or communicate with any computing device as described in thisdisclosure, including without limitation a microcontroller,microprocessor, digital signal processor (DSP) and/or system on a chip(SoC) as described in this disclosure. Further, flight controller 804may include a single computing device operating independently, or mayinclude two or more computing device operating in concert, in parallel,sequentially or the like; two or more computing devices may be includedtogether in a single computing device or in two or more computingdevices. In embodiments, flight controller 804 may be installed in anaircraft, may control the aircraft remotely, and/or may include anelement installed in the aircraft and a remote element in communicationtherewith.

In an embodiment, and still referring to FIG. 8 , flight controller 804may include a signal transformation component 808. As used in thisdisclosure a “signal transformation component” is a component thattransforms and/or converts a first signal to a second signal, wherein asignal may include one or more digital and/or analog signals. Forexample, and without limitation, signal transformation component 808 maybe configured to perform one or more operations such as preprocessing,lexical analysis, parsing, semantic analysis, and the like thereof. Inan embodiment, and without limitation, signal transformation component808 may include one or more analog-to-digital convertors that transforma first signal of an analog signal to a second signal of a digitalsignal. For example, and without limitation, an analog-to-digitalconverter may convert an analog input signal to an 8-bit binary digitalrepresentation of that signal. In another embodiment, signaltransformation component 808 may include transforming one or morelow-level languages such as, but not limited to, machine languagesand/or assembly languages. For example, and without limitation, signaltransformation component 808 may include transforming a binary languagesignal to an assembly language signal. In an embodiment, and withoutlimitation, signal transformation component 808 may include transformingone or more high-level languages and/or formal languages such as but notlimited to alphabets, strings, and/or languages. For example, andwithout limitation, high-level languages may include one or more systemlanguages, scripting languages, domain-specific languages, visuallanguages, esoteric languages, and the like thereof. As a furthernon-limiting example, high-level languages may include one or morealgebraic formula languages, business data languages, string and listlanguages, object-oriented languages, and the like thereof

Still referring to FIG. 8 , signal transformation component 808 may beconfigured to optimize an intermediate representation 812. As used inthis disclosure an “intermediate representation” is a data structureand/or code that represents the input signal. Signal transformationcomponent 808 may optimize intermediate representation as a function ofa data-flow analysis, dependence analysis, alias analysis, pointeranalysis, escape analysis, and the like thereof. In an embodiment, andwithout limitation, signal transformation component 808 may optimizeintermediate representation 812 as a function of one or more inlineexpansions, dead code eliminations, constant propagation, looptransformations, and/or automatic parallelization functions. In anotherembodiment, signal transformation component 808 may optimizeintermediate representation as a function of a machine dependentoptimization such as a peephole optimization, wherein a peepholeoptimization may rewrite short sequences of code into more efficientsequences of code. Signal transformation component 808 may optimizeintermediate representation to generate an output language, wherein an“output language,” as used herein, is the native machine language offlight controller 804. For example, and without limitation, nativemachine language may include one or more binary and/or numericallanguages.

In an embodiment, and without limitation, signal transformationcomponent 808 may include transform one or more inputs and outputs as afunction of an error correction code. An error correction code, alsoknown as error correcting code (ECC), is an encoding of a message or lotof data using redundant information, permitting recovery of corrupteddata. An ECC may include a block code, in which information is encodedon fixed-size packets and/or blocks of data elements such as symbols ofpredetermined size, bits, or the like. Reed-Solomon coding, in whichmessage symbols within a symbol set having q symbols are encoded ascoefficients of a polynomial of degree less than or equal to a naturalnumber k, over a finite field F with q elements; strings so encoded havea minimum hamming distance of k+1, and permit correction of (q−k−1)/2erroneous symbols. Block code may alternatively or additionally beimplemented using Golay coding, also known as binary Golay coding,Bose-Chaudhuri, Hocquenghuem (BCH) coding, multidimensional parity-checkcoding, and/or Hamming codes. An ECC may alternatively or additionallybe based on a convolutional code.

In an embodiment, and still referring to FIG. 8 , flight controller 804may include a reconfigurable hardware platform 816. A “reconfigurablehardware platform,” as used herein, is a component and/or unit ofhardware that may be reprogrammed, such that, for instance, a data pathbetween elements such as logic gates or other digital circuit elementsmay be modified to change an algorithm, state, logical sequence, or thelike of the component and/or unit. This may be accomplished with suchflexible high-speed computing fabrics as field-programmable gate arrays(FPGAs), which may include a grid of interconnected logic gates,connections between which may be severed and/or restored to program inmodified logic. Reconfigurable hardware platform 816 may be reconfiguredto enact any algorithm and/or algorithm selection process received fromanother computing device and/or created using machine-learningprocesses.

Still referring to FIG. 8 , reconfigurable hardware platform 816 mayinclude a logic component 520. As used in this disclosure a “logiccomponent” is a component that executes instructions on output language.For example, and without limitation, logic component may perform basicarithmetic, logic, controlling, input/output operations, and the likethereof. Logic component 520 may include any suitable processor, such aswithout limitation a component incorporating logical circuitry forperforming arithmetic and logical operations, such as an arithmetic andlogic unit (ALU), which may be regulated with a state machine anddirected by operational inputs from memory and/or sensors; logiccomponent 520 may be organized according to Von Neumann and/or Harvardarchitecture as a non-limiting example. Logic component 520 may include,incorporate, and/or be incorporated in, without limitation, amicrocontroller, microprocessor, digital signal processor (DSP), FieldProgrammable Gate Array (FPGA), Complex Programmable Logic Device(CPLD), Graphical Processing Unit (GPU), general purpose GPU, TensorProcessing Unit (TPU), analog or mixed signal processor, TrustedPlatform Module (TPM), a floating-point unit (FPU), and/or system on achip (SoC). In an embodiment, logic component 520 may include one ormore integrated circuit microprocessors, which may contain one or morecentral processing units, central processors, and/or main processors, ona single metal-oxide-semiconductor chip. Logic component 520 may beconfigured to execute a sequence of stored instructions to be performedon the output language and/or intermediate representation 812. Logiccomponent 520 may be configured to fetch and/or retrieve the instructionfrom a memory cache, wherein a “memory cache,” as used in thisdisclosure, is a stored instruction set on flight controller 804. Logiccomponent 520 may be configured to decode the instruction retrieved fromthe memory cache to opcodes and/or operands. Logic component 520 may beconfigured to execute the instruction on intermediate representation 812and/or output language. For example, and without limitation, logiccomponent 520 may be configured to execute an addition operation onintermediate representation 812 and/or output language.

In an embodiment, and without limitation, logic component 520 may beconfigured to calculate a flight element 524. As used in this disclosurea “flight element” is an element of datum denoting a relative status ofaircraft. For example, and without limitation, flight element 524 maydenote one or more torques, thrusts, airspeed velocities, forces,altitudes, groundspeed velocities, directions during flight, directionsfacing, forces, orientations, and the like thereof. For example, andwithout limitation, flight element 524 may denote that aircraft iscruising at an altitude and/or with a sufficient magnitude of forwardthrust. As a further non-limiting example, flight status may denote thatis building thrust and/or groundspeed velocity in preparation for atakeoff. As a further non-limiting example, flight element 524 maydenote that aircraft is following a flight path accurately and/orsufficiently.

Still referring to FIG. 8 , flight controller 804 may include a chipsetcomponent 528. As used in this disclosure a “chipset component” is acomponent that manages data flow. In an embodiment, and withoutlimitation, chipset component 528 may include a northbridge data flowpath, wherein the northbridge dataflow path may manage data flow fromlogic component 520 to a high-speed device and/or component, such as aRAM, graphics controller, and the like thereof. In another embodiment,and without limitation, chipset component 528 may include a southbridgedata flow path, wherein the southbridge dataflow path may manage dataflow from logic component 520 to lower-speed peripheral buses, such as aperipheral component interconnect (PCI), industry standard architecture(ICA), and the like thereof. In an embodiment, and without limitation,southbridge data flow path may include managing data flow betweenperipheral connections such as ethernet, USB, audio devices, and thelike thereof. Additionally or alternatively, chipset component 528 maymanage data flow between logic component 520, memory cache, and a flightcomponent 532. As used in this disclosure a “flight component” is aportion of an aircraft that can be moved or adjusted to affect one ormore flight elements. For example, flight component 532 may include acomponent used to affect the aircrafts' roll and pitch which maycomprise one or more ailerons. As a further example, flight component532 may include a rudder to control yaw of an aircraft. In anembodiment, chipset component 528 may be configured to communicate witha plurality of flight components as a function of flight element 524.For example, and without limitation, chipset component 528 may transmitto an aircraft rotor to reduce torque of a first lift propulsor andincrease the forward thrust produced by a pusher component to perform aflight maneuver.

In an embodiment, and still referring to FIG. 8 , flight controller 804may be configured generate an autonomous function. As used in thisdisclosure an “autonomous function” is a mode and/or function of flightcontroller 804 that controls aircraft automatically. For example, andwithout limitation, autonomous function may perform one or more aircraftmaneuvers, take offs, landings, altitude adjustments, flight levelingadjustments, turns, climbs, and/or descents. As a further non-limitingexample, autonomous function may adjust one or more airspeed velocities,thrusts, torques, and/or groundspeed velocities. As a furthernon-limiting example, autonomous function may perform one or more flightpath corrections and/or flight path modifications as a function offlight element 524. In an embodiment, autonomous function may includeone or more modes of autonomy such as, but not limited to, autonomousmode, semi-autonomous mode, and/or non-autonomous mode. As used in thisdisclosure “autonomous mode” is a mode that automatically adjusts and/orcontrols aircraft and/or the maneuvers of aircraft in its entirety. Forexample, autonomous mode may denote that flight controller 804 willadjust the aircraft. As used in this disclosure a “semi-autonomous mode”is a mode that automatically adjusts and/or controls a portion and/orsection of aircraft. For example, and without limitation,semi-autonomous mode may denote that a pilot will control thepropulsors, wherein flight controller 804 will control the aileronsand/or rudders. As used in this disclosure “non-autonomous mode” is amode that denotes a pilot will control aircraft and/or maneuvers ofaircraft in its entirety.

In an embodiment, and still referring to FIG. 8 , flight controller 804may generate autonomous function as a function of an autonomousmachine-learning model. As used in this disclosure an “autonomousmachine-learning model” is a machine-learning model to produce anautonomous function output given flight element 524 and a pilot signal536 as inputs; this is in contrast to a non-machine learning softwareprogram where the commands to be executed are determined in advance by auser and written in a programming language. As used in this disclosure a“pilot signal” is an element of datum representing one or more functionsa pilot is controlling and/or adjusting. For example, pilot signal 536may denote that a pilot is controlling and/or maneuvering ailerons,wherein the pilot is not in control of the rudders and/or propulsors. Inan embodiment, pilot signal 536 may include an implicit signal and/or anexplicit signal. For example, and without limitation, pilot signal 536may include an explicit signal, wherein the pilot explicitly statesthere is a lack of control and/or desire for autonomous function. As afurther non-limiting example, pilot signal 536 may include an explicitsignal directing flight controller 804 to control and/or maintain aportion of aircraft, a portion of the flight plan, the entire aircraft,and/or the entire flight plan. As a further non-limiting example, pilotsignal 536 may include an implicit signal, wherein flight controller 804detects a lack of control such as by a malfunction, torque alteration,flight path deviation, and the like thereof. In an embodiment, andwithout limitation, pilot signal 536 may include one or more explicitsignals to reduce torque, and/or one or more implicit signals thattorque may be reduced due to reduction of airspeed velocity. In anembodiment, and without limitation, pilot signal 536 may include one ormore local and/or global signals. For example, and without limitation,pilot signal 536 may include a local signal that is transmitted by apilot and/or crew member. As a further non-limiting example, pilotsignal 536 may include a global signal that is transmitted by airtraffic control and/or one or more remote users that are incommunication with the pilot of aircraft. In an embodiment, pilot signal536 may be received as a function of a tri-state bus and/or multiplexorthat denotes an explicit pilot signal should be transmitted prior to anyimplicit or global pilot signal.

Still referring to FIG. 8 , autonomous machine-learning model mayinclude one or more autonomous machine-learning processes such assupervised, unsupervised, or reinforcement machine-learning processesthat flight controller 804 and/or a remote device may or may not use inthe generation of autonomous function. As used in this disclosure“remote device” is an external device to flight controller 804.Additionally or alternatively, autonomous machine-learning model mayinclude one or more autonomous machine-learning processes that afield-programmable gate array (FPGA) may or may not use in thegeneration of autonomous function. Autonomous machine-learning processmay include, without limitation machine learning processes such assimple linear regression, multiple linear regression, polynomialregression, support vector regression, ridge regression, lassoregression, elasticnet regression, decision tree regression, randomforest regression, logistic regression, logistic classification,K-nearest neighbors, support vector machines, kernel support vectormachines, naive bayes, decision tree classification, random forestclassification, K-means clustering, hierarchical clustering,dimensionality reduction, principal component analysis, lineardiscriminant analysis, kernel principal component analysis, Q-learning,State Action Reward State Action (SARSA), Deep-Q network, Markovdecision processes, Deep Deterministic Policy Gradient (DDPG), or thelike thereof.

In an embodiment, and still referring to FIG. 8 , autonomous machinelearning model may be trained as a function of autonomous training data,wherein autonomous training data may correlate a flight element, pilotsignal, and/or simulation data to an autonomous function. For example,and without limitation, a flight element of an airspeed velocity, apilot signal of limited and/or no control of propulsors, and asimulation data of required airspeed velocity to reach the destinationmay result in an autonomous function that includes a semi-autonomousmode to increase thrust of the propulsors. Autonomous training data maybe received as a function of user-entered valuations of flight elements,pilot signals, simulation data, and/or autonomous functions. Flightcontroller 804 may receive autonomous training data by receivingcorrelations of flight element, pilot signal, and/or simulation data toan autonomous function that were previously received and/or determinedduring a previous iteration of generation of autonomous function.Autonomous training data may be received by one or more remote devicesand/or FPGAs that at least correlate a flight element, pilot signal,and/or simulation data to an autonomous function. Autonomous trainingdata may be received in the form of one or more user-enteredcorrelations of a flight element, pilot signal, and/or simulation datato an autonomous function.

Still referring to FIG. 8 , flight controller 804 may receive autonomousmachine-learning model from a remote device and/or FPGA that utilizesone or more autonomous machine learning processes, wherein a remotedevice and an FPGA is described above in detail. For example, andwithout limitation, a remote device may include a computing device,external device, processor, FPGA, microprocessor and the like thereof.Remote device and/or FPGA may perform the autonomous machine-learningprocess using autonomous training data to generate autonomous functionand transmit the output to flight controller 804. Remote device and/orFPGA may transmit a signal, bit, datum, or parameter to flightcontroller 804 that at least relates to autonomous function.Additionally or alternatively, the remote device and/or FPGA may providean updated machine-learning model. For example, and without limitation,an updated machine-learning model may be comprised of a firmware update,a software update, an autonomous machine-learning process correction,and the like thereof. As a non-limiting example a software update mayincorporate a new simulation data that relates to a modified flightelement. Additionally or alternatively, the updated machine learningmodel may be transmitted to the remote device and/or FPGA, wherein theremote device and/or FPGA may replace the autonomous machine-learningmodel with the updated machine-learning model and generate theautonomous function as a function of the flight element, pilot signal,and/or simulation data using the updated machine-learning model. Theupdated machine-learning model may be transmitted by the remote deviceand/or FPGA and received by flight controller 804 as a software update,firmware update, or corrected autonomous machine-learning model. Forexample, and without limitation autonomous machine learning model mayutilize a neural net machine-learning process, wherein the updatedmachine-learning model may incorporate a gradient boostingmachine-learning process.

Still referring to FIG. 8 , flight controller 804 may include, beincluded in, and/or communicate with a mobile device such as a mobiletelephone or smartphone. Further, flight controller may communicate withone or more additional devices as described below in further detail viaa network interface device. The network interface device may be utilizedfor commutatively connecting a flight controller to one or more of avariety of networks, and one or more devices.

Examples of a network interface device include, but are not limited to,a network interface card (e.g., a mobile network interface card, a LANcard), a modem, and any combination thereof. Examples of a networkinclude, but are not limited to, a wide area network (e.g., theInternet, an enterprise network), a local area network (e.g., a networkassociated with an office, a building, a campus or other relativelysmall geographic space), a telephone network, a data network associatedwith a telephone/voice provider (e.g., a mobile communications providerdata and/or voice network), a direct connection between two computingdevices, and any combinations thereof. The network may include anynetwork topology and can may employ a wired and/or a wireless mode ofcommunication.

In an embodiment, and still referring to FIG. 8 , flight controller 804may include, but is not limited to, for example, a cluster of flightcontrollers in a first location and a second flight controller orcluster of flight controllers in a second location. Flight controller804 may include one or more flight controllers dedicated to datastorage, security, distribution of traffic for load balancing, and thelike. Flight controller 804 may be configured to distribute one or morecomputing tasks as described below across a plurality of flightcontrollers, which may operate in parallel, in series, redundantly, orin any other manner used for distribution of tasks or memory betweencomputing devices. For example, and without limitation, flightcontroller 804 may implement a control algorithm to distribute and/orcommand the plurality of flight controllers. As used in this disclosurea “control algorithm” is a finite sequence of well-defined computerimplementable instructions that may determine the flight component ofthe plurality of flight components to be adjusted. For example, andwithout limitation, control algorithm may include one or more algorithmsthat reduce and/or prevent aviation asymmetry. As a further non-limitingexample, control algorithms may include one or more models generated asa function of a software including, but not limited to Simulink byMathWorks, Natick, Massachusetts, USA. In an embodiment, and withoutlimitation, control algorithm may be configured to generate anauto-code, wherein an “auto-code,” is used herein, is a code and/oralgorithm that is generated as a function of the one or more modelsand/or software's. In another embodiment, control algorithm may beconfigured to produce a segmented control algorithm. As used in thisdisclosure a “segmented control algorithm” is control algorithm that hasbeen separated and/or parsed into discrete sections. For example, andwithout limitation, segmented control algorithm may parse controlalgorithm into two or more segments, wherein each segment of controlalgorithm may be performed by one or more flight controllers operatingon distinct flight components.

In an embodiment, and still referring to FIG. 8 , control algorithm maybe configured to determine a segmentation boundary as a function ofsegmented control algorithm. As used in this disclosure a “segmentationboundary” is a limit and/or delineation associated with the segments ofthe segmented control algorithm. For example, and without limitation,segmentation boundary may denote that a segment in the control algorithmhas a first starting section and/or a first ending section. As a furthernon-limiting example, segmentation boundary may include one or moreboundaries associated with an ability of flight component 532. In anembodiment, control algorithm may be configured to create an optimizedsignal communication as a function of segmentation boundary. Forexample, and without limitation, optimized signal communication mayinclude identifying the discrete timing required to transmit and/orreceive the one or more segmentation boundaries. In an embodiment, andwithout limitation, creating optimized signal communication furthercomprises separating a plurality of signal codes across the plurality offlight controllers. For example, and without limitation the plurality offlight controllers may include one or more formal networks, whereinformal networks transmit data along an authority chain and/or arelimited to task-related communications. As a further non-limitingexample, communication network may include informal networks, whereininformal networks transmit data in any direction. In an embodiment, andwithout limitation, the plurality of flight controllers may include achain path, wherein a “chain path,” as used herein, is a linearcommunication path comprising a hierarchy that data may flow through. Inan embodiment, and without limitation, the plurality of flightcontrollers may include an all-channel path, wherein an “all-channelpath,” as used herein, is a communication path that is not restricted toa particular direction. For example, and without limitation, data may betransmitted upward, downward, laterally, and the like thereof. In anembodiment, and without limitation, the plurality of flight controllersmay include one or more neural networks that assign a weighted value toa transmitted datum. For example, and without limitation, a weightedvalue may be assigned as a function of one or more signals denoting thata flight component is malfunctioning and/or in a failure state.

Still referring to FIG. 8 , the plurality of flight controllers mayinclude a master bus controller. As used in this disclosure a “masterbus controller” is one or more devices and/or components that areconnected to a bus to initiate a direct memory access transaction,wherein a bus is one or more terminals in a bus architecture. Master buscontroller may communicate using synchronous and/or asynchronous buscontrol protocols. In an embodiment, master bus controller may includeflight controller 804. In another embodiment, master bus controller mayinclude one or more universal asynchronous receiver-transmitters (UART).For example, and without limitation, master bus controller may includeone or more bus architectures that allow a bus to initiate a directmemory access transaction from one or more buses in the busarchitectures. As a further non-limiting example, master bus controllermay include one or more peripheral devices and/or components tocommunicate with another peripheral device and/or component and/or themaster bus controller. In an embodiment, master bus controller may beconfigured to perform bus arbitration. As used in this disclosure “busarbitration” is method and/or scheme to prevent multiple buses fromattempting to communicate with and/or connect to master bus controller.For example and without limitation, bus arbitration may include one ormore schemes such as a small computer interface system, wherein a smallcomputer interface system is a set of standards for physical connectingand transferring data between peripheral devices and master buscontroller by defining commands, protocols, electrical, optical, and/orlogical interfaces. In an embodiment, master bus controller may receiveintermediate representation 812 and/or output language from logiccomponent 520, wherein output language may include one or moreanalog-to-digital conversions, low bit rate transmissions, messageencryptions, digital signals, binary signals, logic signals, analogsignals, and the like thereof described above in detail.

Still referring to FIG. 8 , master bus controller may communicate with aslave bus. As used in this disclosure a “slave bus” is one or moreperipheral devices and/or components that initiate a bus transfer. Forexample, and without limitation, slave bus may receive one or morecontrols and/or asymmetric communications from master bus controller,wherein slave bus transfers data stored to master bus controller. In anembodiment, and without limitation, slave bus may include one or moreinternal buses, such as but not limited to a/an internal data bus,memory bus, system bus, front-side bus, and the like thereof. In anotherembodiment, and without limitation, slave bus may include one or moreexternal buses such as external flight controllers, external computers,remote devices, printers, aircraft computer systems, flight controlsystems, and the like thereof.

In an embodiment, and still referring to FIG. 8 , control algorithm mayoptimize signal communication as a function of determining one or morediscrete timings. For example, and without limitation master buscontroller may synchronize timing of the segmented control algorithm byinjecting high priority timing signals on a bus of the master buscontrol. As used in this disclosure a “high priority timing signal” isinformation denoting that the information is important. For example, andwithout limitation, high priority timing signal may denote that asection of control algorithm is of high priority and should be analyzedand/or transmitted prior to any other sections being analyzed and/ortransmitted. In an embodiment, high priority timing signal may includeone or more priority packets. As used in this disclosure a “prioritypacket” is a formatted unit of data that is communicated between theplurality of flight controllers. For example, and without limitation,priority packet may denote that a section of control algorithm should beused and/or is of greater priority than other sections.

Still referring to FIG. 8 , flight controller 804 may also beimplemented using a “shared nothing” architecture in which data iscached at the worker, in an embodiment, this may enable scalability ofaircraft and/or computing device. Flight controller 804 may include adistributer flight controller. As used in this disclosure a “distributerflight controller” is a component that adjusts and/or controls aplurality of flight components as a function of a plurality of flightcontrollers. For example, distributer flight controller may include aflight controller that communicates with a plurality of additionalflight controllers and/or clusters of flight controllers. In anembodiment, distributed flight control may include one or more neuralnetworks. For example, neural network also known as an artificial neuralnetwork, is a network of “nodes,” or data structures having one or moreinputs, one or more outputs, and a function determining outputs based oninputs. Such nodes may be organized in a network, such as withoutlimitation a convolutional neural network, including an input layer ofnodes, one or more intermediate layers, and an output layer of nodes.Connections between nodes may be created via the process of “training”the network, in which elements from a training dataset are applied tothe input nodes, a suitable training algorithm (such asLevenberg-Marquardt, conjugate gradient, simulated annealing, or otheralgorithms) is then used to adjust the connections and weights betweennodes in adjacent layers of the neural network to produce the desiredvalues at the output nodes. This process is sometimes referred to asdeep learning.

Still referring to FIG. 8 , a node may include, without limitation aplurality of inputs x_(i) that may receive numerical values from inputsto a neural network containing the node and/or from other nodes. Nodemay perform a weighted sum of inputs using weights w_(i) that aremultiplied by respective inputs x_(i). Additionally or alternatively, abias b may be added to the weighted sum of the inputs such that anoffset is added to each unit in the neural network layer that isindependent of the input to the layer. The weighted sum may then beinput into a function φ, which may generate one or more outputs y.Weight w_(i) applied to an input x_(i) may indicate whether the input is“excitatory,” indicating that it has strong influence on the one or moreoutputs y, for instance by the corresponding weight having a largenumerical value, and/or a “inhibitory,” indicating it has a weak effectinfluence on the one more inputs y, for instance by the correspondingweight having a small numerical value. The values of weights w_(i) maybe determined by training a neural network using training data, whichmay be performed using any suitable process as described above. In anembodiment, and without limitation, a neural network may receivesemantic units as inputs and output vectors representing such semanticunits according to weights w_(i) that are derived using machine-learningprocesses as described in this disclosure.

Still referring to FIG. 8 , flight controller may include asub-controller 540. As used in this disclosure a “sub-controller” is acontroller and/or component that is part of a distributed controller asdescribed above; for instance, flight controller 804 may be and/orinclude a distributed flight controller made up of one or moresub-controllers. For example, and without limitation, sub-controller 540may include any controllers and/or components thereof that are similarto distributed flight controller and/or flight controller as describedabove. Sub-controller 540 may include any component of any flightcontroller as described above. Sub-controller 540 may be implemented inany manner suitable for implementation of a flight controller asdescribed above. As a further non-limiting example, sub-controller 540may include one or more processors, logic components and/or computingdevices capable of receiving, processing, and/or transmitting dataacross the distributed flight controller as described above. As afurther non-limiting example, sub-controller 540 may include acontroller that receives a signal from a first flight controller and/orfirst distributed flight controller component and transmits the signalto a plurality of additional sub-controllers and/or flight components.

Still referring to FIG. 8 , flight controller may include aco-controller 544. As used in this disclosure a “co-controller” is acontroller and/or component that joins flight controller 804 ascomponents and/or nodes of a distributer flight controller as describedabove. For example, and without limitation, co-controller 544 mayinclude one or more controllers and/or components that are similar toflight controller 804. As a further non-limiting example, co-controller544 may include any controller and/or component that joins flightcontroller 804 to distributer flight controller. As a furthernon-limiting example, co-controller 544 may include one or moreprocessors, logic components and/or computing devices capable ofreceiving, processing, and/or transmitting data to and/or from flightcontroller 804 to distributed flight control system. Co-controller 544may include any component of any flight controller as described above.Co-controller 544 may be implemented in any manner suitable forimplementation of a flight controller as described above. In anembodiment, and with continued reference to FIG. 8 , flight controller804 may be designed and/or configured to perform any method, methodstep, or sequence of method steps in any embodiment described in thisdisclosure, in any order and with any degree of repetition. Forinstance, flight controller 804 may be configured to perform a singlestep or sequence repeatedly until a desired or commanded outcome isachieved; repetition of a step or a sequence of steps may be performediteratively and/or recursively using outputs of previous repetitions asinputs to subsequent repetitions, aggregating inputs and/or outputs ofrepetitions to produce an aggregate result, reduction or decrement ofone or more variables such as global variables, and/or division of alarger processing task into a set of iteratively addressed smallerprocessing tasks. Flight controller may perform any step or sequence ofsteps as described in this disclosure in parallel, such assimultaneously and/or substantially simultaneously performing a step twoor more times using two or more parallel threads, processor cores, orthe like; division of tasks between parallel threads and/or processesmay be performed according to any protocol suitable for division oftasks between iterations. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various ways in whichsteps, sequences of steps, processing tasks, and/or data may besubdivided, shared, or otherwise dealt with using iteration, recursion,and/or parallel processing.

Referring now to FIG. 9 , an exemplary embodiment of a machine-learningmodule 900 that may perform one or more machine-learning processes asdescribed in this disclosure is illustrated. Machine-learning module mayperform determinations, classification, and/or analysis steps, methods,processes, or the like as described in this disclosure using machinelearning processes. A “machine learning process,” as used in thisdisclosure, is a process that automatedly uses training data 904 togenerate an algorithm that will be performed by a computingdevice/module to produce outputs 908 given data provided as inputs 912;this is in contrast to a non-machine learning software program where thecommands to be executed are determined in advance by a user and writtenin a programming language.

Still referring to FIG. 9 , “training data,” as used herein, is datacontaining correlations that a machine-learning process may use to modelrelationships between two or more categories of data elements. Forinstance, and without limitation, training data 904 may include aplurality of data entries, each entry representing a set of dataelements that were recorded, received, and/or generated together; dataelements may be correlated by shared existence in a given data entry, byproximity in a given data entry, or the like. Multiple data entries intraining data 904 may evince one or more trends in correlations betweencategories of data elements; for instance, and without limitation, ahigher value of a first data element belonging to a first category ofdata element may tend to correlate to a higher value of a second dataelement belonging to a second category of data element, indicating apossible proportional or other mathematical relationship linking valuesbelonging to the two categories. Multiple categories of data elementsmay be related in training data 904 according to various correlations;correlations may indicate causative and/or predictive links betweencategories of data elements, which may be modeled as relationships suchas mathematical relationships by machine-learning processes as describedin further detail below. Training data 904 may be formatted and/ororganized by categories of data elements, for instance by associatingdata elements with one or more descriptors corresponding to categoriesof data elements. As a non-limiting example, training data 904 mayinclude data entered in standardized forms by persons or processes, suchthat entry of a given data element in a given field in a form may bemapped to one or more descriptors of categories. Elements in trainingdata 904 may be linked to descriptors of categories by tags, tokens, orother data elements; for instance, and without limitation, training data904 may be provided in fixed-length formats, formats linking positionsof data to categories such as comma-separated value (CSV) formats and/orself-describing formats such as extensible markup language (XML),JavaScript Object Notation (JSON), or the like, enabling processes ordevices to detect categories of data.

Alternatively or additionally, and continuing to refer to FIG. 9 ,training data 904 may include one or more elements that are notcategorized; that is, training data 904 may not be formatted or containdescriptors for some elements of data. Machine-learning algorithmsand/or other processes may sort training data 904 according to one ormore categorizations using, for instance, natural language processingalgorithms, tokenization, detection of correlated values in raw data andthe like; categories may be generated using correlation and/or otherprocessing algorithms. As a non-limiting example, in a corpus of text,phrases making up a number “n” of compound words, such as nouns modifiedby other nouns, may be identified according to a statisticallysignificant prevalence of n-grams containing such words in a particularorder; such an n-gram may be categorized as an element of language suchas a “word” to be tracked similarly to single words, generating a newcategory as a result of statistical analysis. Similarly, in a data entryincluding some textual data, a person's name may be identified byreference to a list, dictionary, or other compendium of terms,permitting ad-hoc categorization by machine-learning algorithms, and/orautomated association of data in the data entry with descriptors or intoa given format. The ability to categorize data entries automatedly mayenable the same training data 904 to be made applicable for two or moredistinct machine-learning algorithms as described in further detailbelow. Training data 904 used by machine-learning module 900 maycorrelate any input data as described in this disclosure to any outputdata as described in this disclosure. As a non-limiting illustrativeexample, the battery pack datum as an input and output a batterydegradation model.

Further referring to FIG. 9 , training data may be filtered, sorted,and/or selected using one or more supervised and/or unsupervisedmachine-learning processes and/or models as described in further detailbelow; such models may include without limitation a training dataclassifier 916. Training data classifier 916 may include a “classifier,”which as used in this disclosure is a machine-learning model as definedbelow, such as a mathematical model, neural net, or program generated bya machine learning algorithm known as a “classification algorithm,” asdescribed in further detail below, that sorts inputs into categories orbins of data, outputting the categories or bins of data and/or labelsassociated therewith. A classifier may be configured to output at leasta datum that labels or otherwise identifies a set of data that areclustered together, found to be close under a distance metric asdescribed below, or the like. Machine-learning module 900 may generate aclassifier using a classification algorithm, defined as a processeswhereby a computing device and/or any module and/or component operatingthereon derives a classifier from training data 904. Classification maybe performed using, without limitation, linear classifiers such aswithout limitation logistic regression and/or naive Bayes classifiers,nearest neighbor classifiers such as k-nearest neighbors classifiers,support vector machines, least squares support vector machines, fisher'slinear discriminant, quadratic classifiers, decision trees, boostedtrees, random forest classifiers, learning vector quantization, and/orneural network-based classifiers. As a non-limiting example, trainingdata classifier 916 may classify elements of training data to differentprediction models of battery degradation for which a subset of trainingdata may be selected.

Still referring to FIG. 9 , machine-learning module 900 may beconfigured to perform a lazy-learning process 920 and/or protocol, whichmay alternatively be referred to as a “lazy loading” or“call-when-needed” process and/or protocol, may be a process wherebymachine learning is conducted upon receipt of an input to be convertedto an output, by combining the input and training set to derive thealgorithm to be used to produce the output on demand. For instance, aninitial set of simulations may be performed to cover an initialheuristic and/or “first guess” at an output and/or relationship. As anon-limiting example, an initial heuristic may include a ranking ofassociations between inputs and elements of training data 904. Heuristicmay include selecting some number of highest-ranking associations and/ortraining data 904 elements. Lazy learning may implement any suitablelazy learning algorithm, including without limitation a K-nearestneighbors algorithm, a lazy naïve Bayes algorithm, or the like; personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various lazy-learning algorithms that may be applied togenerate outputs as described in this disclosure, including withoutlimitation lazy learning applications of machine-learning algorithms asdescribed in further detail below.

Alternatively or additionally, and with continued reference to FIG. 9 ,machine-learning processes as described in this disclosure may be usedto generate machine-learning models 924. A “machine-learning model,” asused in this disclosure, is a mathematical and/or algorithmicrepresentation of a relationship between inputs and outputs, asgenerated using any machine-learning process including withoutlimitation any process as described above, and stored in memory; aninput is submitted to a machine-learning model 924 once created, whichgenerates an output based on the relationship that was derived. Forinstance, and without limitation, a linear regression model, generatedusing a linear regression algorithm, may compute a linear combination ofinput data using coefficients derived during machine-learning processesto calculate an output datum. As a further non-limiting example, amachine-learning model 924 may be generated by creating an artificialneural network, such as a convolutional neural network comprising aninput layer of nodes, one or more intermediate layers, and an outputlayer of nodes. Connections between nodes may be created via the processof “training” the network, in which elements from a training data 904set are applied to the input nodes, a suitable training algorithm (suchas Levenberg-Marquardt, conjugate gradient, simulated annealing, orother algorithms) is then used to adjust the connections and weightsbetween nodes in adjacent layers of the neural network to produce thedesired values at the output nodes. This process is sometimes referredto as deep learning.

Still referring to FIG. 9 , machine-learning algorithms may include atleast a supervised machine-learning process 928. At least a supervisedmachine-learning process 928, as defined herein, include algorithms thatreceive a training set relating a number of inputs to a number ofoutputs, and seek to find one or more mathematical relations relatinginputs to outputs, where each of the one or more mathematical relationsis optimal according to some criterion specified to the algorithm usingsome scoring function. For instance, a supervised learning algorithm mayinclude any battery pack datum which may be retrieved from the batterydatabase as described above as inputs, any battery pack model or batterydegradation model from the battery database as outputs, and a scoringfunction representing a desired form of relationship to be detectedbetween inputs and outputs; scoring function may, for instance, seek tomaximize the probability that a given input and/or combination ofelements inputs is associated with a given output to minimize theprobability that a given input is not associated with a given output.Scoring function may be expressed as a risk function representing an“expected loss” of an algorithm relating inputs to outputs, where lossis computed as an error function representing a degree to which aprediction generated by the relation is incorrect when compared to agiven input-output pair provided in training data 904. Persons skilledin the art, upon reviewing the entirety of this disclosure, will beaware of various possible variations of at least a supervisedmachine-learning process 928 that may be used to determine relationbetween inputs and outputs. Supervised machine-learning processes mayinclude classification algorithms as defined above.

Further referring to FIG. 9 , machine learning processes may include atleast an unsupervised machine-learning processes 932. An unsupervisedmachine-learning process, as used herein, is a process that derivesinferences in datasets without regard to labels; as a result, anunsupervised machine-learning process may be free to discover anystructure, relationship, and/or correlation provided in the data.Unsupervised processes may not require a response variable; unsupervisedprocesses may be used to find interesting patterns and/or inferencesbetween variables, to determine a degree of correlation between two ormore variables, or the like.

Still referring to FIG. 9 , machine-learning module 900 may be designedand configured to create a machine-learning model 924 using techniquesfor development of linear regression models. Linear regression modelsmay include ordinary least squares regression, which aims to minimizethe square of the difference between predicted outcomes and actualoutcomes according to an appropriate norm for measuring such adifference (e.g. a vector-space distance norm); coefficients of theresulting linear equation may be modified to improve minimization.Linear regression models may include ridge regression methods, where thefunction to be minimized includes the least-squares function plus termmultiplying the square of each coefficient by a scalar amount topenalize large coefficients. Linear regression models may include leastabsolute shrinkage and selection operator (LASSO) models, in which ridgeregression is combined with multiplying the least-squares term by afactor of 1 divided by double the number of samples. Linear regressionmodels may include a multi-task lasso model wherein the norm applied inthe least-squares term of the lasso model is the Frobenius normamounting to the square root of the sum of squares of all terms. Linearregression models may include the elastic net model, a multi-taskelastic net model, a least angle regression model, a LARS lasso model,an orthogonal matching pursuit model, a Bayesian regression model, alogistic regression model, a stochastic gradient descent model, aperceptron model, a passive aggressive algorithm, a robustnessregression model, a Huber regression model, or any other suitable modelthat may occur to persons skilled in the art upon reviewing the entiretyof this disclosure. Linear regression models may be generalized in anembodiment to polynomial regression models, whereby a polynomialequation (e.g. a quadratic, cubic or higher-order equation) providing abest predicted output/actual output fit is sought; similar methods tothose described above may be applied to minimize error functions, aswill be apparent to persons skilled in the art upon reviewing theentirety of this disclosure.

Continuing to refer to FIG. 9 , machine-learning algorithms may include,without limitation, linear discriminant analysis. Machine-learningalgorithm may include quadratic discriminate analysis. Machine-learningalgorithms may include kernel ridge regression. Machine-learningalgorithms may include support vector machines, including withoutlimitation support vector classification-based regression processes.Machine-learning algorithms may include stochastic gradient descentalgorithms, including classification and regression algorithms based onstochastic gradient descent. Machine-learning algorithms may includenearest neighbors algorithms. Machine-learning algorithms may includevarious forms of latent space regularization such as variationalregularization. Machine-learning algorithms may include Gaussianprocesses such as Gaussian Process Regression. Machine-learningalgorithms may include cross-decomposition algorithms, including partialleast squares and/or canonical correlation analysis. Machine-learningalgorithms may include naive Bayes methods. Machine-learning algorithmsmay include algorithms based on decision trees, such as decision treeclassification or regression algorithms. Machine-learning algorithms mayinclude ensemble methods such as bagging meta-estimator, forest ofrandomized tress, AdaBoost, gradient tree boosting, and/or votingclassifier methods. Machine-learning algorithms may include neural netalgorithms, including convolutional neural net processes.

It is to be noted that any one or more of the aspects and embodimentsdescribed herein may be conveniently implemented using one or moremachines (e.g., one or more computing devices that are utilized as auser computing device for an electronic document, one or more serverdevices, such as a document server, etc.) programmed according to theteachings of the present specification, as will be apparent to those ofordinary skill in the computer art. Appropriate software coding canreadily be prepared by skilled programmers based on the teachings of thepresent disclosure, as will be apparent to those of ordinary skill inthe software art. Aspects and implementations discussed above employingsoftware and/or software modules may also include appropriate hardwarefor assisting in the implementation of the machine executableinstructions of the software and/or software module.

Such software may be a computer program product that employs amachine-readable storage medium. A machine-readable storage medium maybe any medium that is capable of storing and/or encoding a sequence ofinstructions for execution by a machine (e.g., a computing device) andthat causes the machine to perform any one of the methodologies and/orembodiments described herein. Examples of a machine-readable storagemedium include, but are not limited to, a magnetic disk, an optical disc(e.g., CD, CD-R, DVD, DVD-R, etc.), a magneto-optical disk, a read-onlymemory “ROM” device, a random access memory “RAM” device, a magneticcard, an optical card, a solid-state memory device, an EPROM, an EEPROM,and any combinations thereof. A machine-readable medium, as used herein,is intended to include a single medium as well as a collection ofphysically separate media, such as, for example, a collection of compactdiscs or one or more hard disk drives in combination with a computermemory. As used herein, a machine-readable storage medium does notinclude transitory forms of signal transmission.

Such software may also include information (e.g., data) carried as adata signal on a data carrier, such as a carrier wave. For example,machine-executable information may be included as a data-carrying signalembodied in a data carrier in which the signal encodes a sequence ofinstruction, or portion thereof, for execution by a machine (e.g., acomputing device) and any related information (e.g., data structures anddata) that causes the machine to perform any one of the methodologiesand/or embodiments described herein.

Examples of a computing device include, but are not limited to, anelectronic book reading device, a computer workstation, a terminalcomputer, a server computer, a handheld device (e.g., a tablet computer,a smartphone, etc.), a web appliance, a network router, a networkswitch, a network bridge, any machine capable of executing a sequence ofinstructions that specify an action to be taken by that machine, and anycombinations thereof. In one example, a computing device may includeand/or be included in a kiosk.

FIG. 10 shows a diagrammatic representation of one embodiment of acomputing device in the exemplary form of a computer system 1000 withinwhich a set of instructions for causing a control system to perform anyone or more of the aspects and/or methodologies of the presentdisclosure may be executed. It is also contemplated that multiplecomputing devices may be utilized to implement a specially configuredset of instructions for causing one or more of the devices to performany one or more of the aspects and/or methodologies of the presentdisclosure. Computer system 1000 includes a processor 1004 and a memory1008 that communicate with each other, and with other components, via abus 1012. Bus 1012 may include any of several types of bus structuresincluding, but not limited to, a memory bus, a memory controller, aperipheral bus, a local bus, and any combinations thereof, using any ofa variety of bus architectures.

Processor 1004 may include any suitable processor, such as withoutlimitation a processor incorporating logical circuitry for performingarithmetic and logical operations, such as an arithmetic and logic unit(ALU), which may be regulated with a state machine and directed byoperational inputs from memory and/or sensors; processor 1004 may beorganized according to Von Neumann and/or Harvard architecture as anon-limiting example. Processor 1004 may include, incorporate, and/or beincorporated in, without limitation, a microcontroller, microprocessor,digital signal processor (DSP), Field Programmable Gate Array (FPGA),Complex Programmable Logic Device (CPLD), Graphical Processing Unit(GPU), general purpose GPU, Tensor Processing Unit (TPU), analog ormixed signal processor, Trusted Platform Module (TPM), a floating pointunit (FPU), and/or system on a chip (SoC).

Memory 1008 may include various components (e.g., machine-readablemedia) including, but not limited to, a random-access memory component,a read only component, and any combinations thereof. In one example, abasic input/output system 1016 (BIOS), including basic routines thathelp to transfer information between elements within computer system1000, such as during start-up, may be stored in memory 1008. Memory 1008may also include (e.g., stored on one or more machine-readable media)instructions (e.g., software) 1020 embodying any one or more of theaspects and/or methodologies of the present disclosure. In anotherexample, memory 1008 may further include any number of program modulesincluding, but not limited to, an operating system, one or moreapplication programs, other program modules, program data, and anycombinations thereof.

Computer system 1000 may also include a storage device 1024. Examples ofa storage device (e.g., storage device 1024) include, but are notlimited to, a hard disk drive, a magnetic disk drive, an optical discdrive in combination with an optical medium, a solid-state memorydevice, and any combinations thereof. Storage device 1024 may beconnected to bus 1012 by an appropriate interface (not shown). Exampleinterfaces include, but are not limited to, SCSI, advanced technologyattachment (ATA), serial ATA, universal serial bus (USB), IEEE 1394(FIREWIRE), and any combinations thereof. In one example, storage device1024 (or one or more components thereof) may be removably interfacedwith computer system 1000 (e.g., via an external port connector (notshown)). Particularly, storage device 1024 and an associatedmachine-readable medium 1028 may provide nonvolatile and/or volatilestorage of machine-readable instructions, data structures, programmodules, and/or other data for computer system 1000. In one example,software 1020 may reside, completely or partially, withinmachine-readable medium 1028. In another example, software 1020 mayreside, completely or partially, within processor 1004.

Computer system 1000 may also include an input device 1032. In oneexample, a user of computer system 1000 may enter commands and/or otherinformation into computer system 1000 via input device 1032. Examples ofan input device 1032 include, but are not limited to, an alpha-numericinput device (e.g., a keyboard), a pointing device, a joystick, agamepad, an audio input device (e.g., a microphone, a voice responsesystem, etc.), a cursor control device (e.g., a mouse), a touchpad, anoptical scanner, a video capture device (e.g., a still camera, a videocamera), a touchscreen, and any combinations thereof. Input device 1032may be interfaced to bus 1012 via any of a variety of interfaces (notshown) including, but not limited to, a serial interface, a parallelinterface, a game port, a USB interface, a FIREWIRE interface, a directinterface to bus 1012, and any combinations thereof. Input device 1032may include a touch screen interface that may be a part of or separatefrom display 1036, discussed further below. Input device 1032 may beutilized as a user selection device for selecting one or more graphicalrepresentations in a graphical interface as described above.

A user may also input commands and/or other information to computersystem 1000 via storage device 1024 (e.g., a removable disk drive, aflash drive, etc.) and/or network interface device 1040. A networkinterface device, such as network interface device 1040, may be utilizedfor connecting computer system 1000 to one or more of a variety ofnetworks, such as network 1044, and one or more remote devices 1048connected thereto. Examples of a network interface device include, butare not limited to, a network interface card (e.g., a mobile networkinterface card, a LAN card), a modem, and any combination thereof.Examples of a network include, but are not limited to, a wide areanetwork (e.g., the Internet, an enterprise network), a local areanetwork (e.g., a network associated with an office, a building, a campusor other relatively small geographic space), a telephone network, a datanetwork associated with a telephone/voice provider (e.g., a mobilecommunications provider data and/or voice network), a direct connectionbetween two computing devices, and any combinations thereof. A network,such as network 1044, may employ a wired and/or a wireless mode ofcommunication. In general, any network topology may be used. Information(e.g., data, software 1020, etc.) may be communicated to and/or fromcomputer system 1000 via network interface device 1040.

Computer system 1000 may further include a video display adapter 1052for communicating a displayable image to a display device, such asdisplay device 1036. Examples of a display device include, but are notlimited to, a liquid crystal display (LCD), a cathode ray tube (CRT), aplasma display, a light emitting diode (LED) display, and anycombinations thereof. Display adapter 1052 and display device 1036 maybe utilized in combination with processor 1004 to provide graphicalrepresentations of aspects of the present disclosure. In addition to adisplay device, computer system 1000 may include one or more otherperipheral output devices including, but not limited to, an audiospeaker, a printer, and any combinations thereof. Such peripheral outputdevices may be connected to bus 1012 via a peripheral interface 1056.Examples of a peripheral interface include, but are not limited to, aserial port, a USB connection, a FIREWIRE connection, a parallelconnection, and any combinations thereof.

The foregoing has been a detailed description of illustrativeembodiments of the invention. Various modifications and additions can bemade without departing from the spirit and scope of this invention.Features of each of the various embodiments described above may becombined with features of other described embodiments as appropriate inorder to provide a multiplicity of feature combinations in associatednew embodiments. Furthermore, while the foregoing describes a number ofseparate embodiments, what has been described herein is merelyillustrative of the application of the principles of the presentinvention. Additionally, although particular methods herein may beillustrated and/or described as being performed in a specific order, theordering is highly variable within ordinary skill to achieve methods,systems, and software according to the present disclosure. Accordingly,this description is meant to be taken only by way of example, and not tootherwise limit the scope of this invention. Exemplary embodiments havebeen disclosed above and illustrated in the accompanying drawings. Itwill be understood by those skilled in the art that various changes,omissions and additions may be made to that which is specificallydisclosed herein without departing from the spirit and scope of thepresent invention.

What is claimed is:
 1. A system for predicting a battery lifetime of abattery during use in an electric vehicle, the system comprising: acomputing device communicatively connected to at least a pack monitorunit, wherein: the at least a pack monitor unit is configured to detecta battery pack datum of a plurality of battery modules incorporated in abattery pack; the computing device is configured to: receive the batterypack datum as a function of the at least a pack monitor unit; generate,as a function of the battery pack datum, a digital twin comprising abattery pack model associated with the battery pack of the electricvehicle; receive periodic instances of battery pack data; periodicallyupdate the digital twin as a function of the received battery pack data;simulate a virtual representation of the digital twin; and predict thebattery lifetime of the battery pack as a function of the virtualrepresentation.
 2. The system of claim 1, wherein the electric vehiclecomprises an electric vertical take-off and landing (eVTOL) aircraft. 3.The system of claim 1, wherein the battery pack datum further comprisesa state of charge of the battery pack.
 4. The system of claim 1, whereinthe battery pack datum further comprises a state of health of thebattery pack.
 5. The system of claim 1, wherein receiving periodicinstances of battery pack data comprises utilizing a timer moduleconfigured to provide specific time intervals for the at least a packmonitor unit to capture the battery pack data.
 6. The system of claim 1,wherein the battery pack model comprises electrochemical model of abattery.
 7. The system of claim 1, wherein the battery lifetimeprediction is further generated as a function of a plurality of batterypack models.
 8. The system of claim 1, wherein predicting a batterylifetime comprises training a machine-learning model with training dataderived from a battery database, wherein the machine-learning model isconfigured to output the battery lifetime prediction.
 9. The system ofclaim 1, wherein the battery pack model further comprises a plurality ofvirtual instances of the battery pack data updated continuously.
 10. Thesystem of claim 9, wherein the plurality of virtual instances is storedin a battery database communicatively connected to a battery managementsystem integrated into the battery pack.
 11. A method for predicting abattery lifetime of a battery during use in an electric vehicle, themethod comprising: detecting, by at least a pack monitor unitcommunicatively connected to a computing device, a battery pack datum ofa plurality of battery modules incorporated in a battery pack;receiving, by the computing device, the battery pack datum from the atleast a pack monitor unit; generating, as a function of the battery packdatum, a digital twin comprising a battery pack model associated withthe battery pack of the electric vehicle; receiving, by the computingdevice, periodic instances of battery pack data; periodically updating,by the computing device, the digital twin as a function of the receivedbattery pack data; simulating a virtual representation of the digitaltwin; and predicting, by the computing device, the battery lifetime ofthe battery pack as a function of the virtual representation.
 12. Themethod of claim 11, wherein the electric vehicle comprises an electricvertical take-off and landing (eVTOL) aircraft.
 13. The method of claim11, wherein detecting the battery pack datum further comprises detectinga state of charge of the battery pack.
 14. The method of claim 11,wherein detecting the battery pack datum further comprises detecting astate of health of the battery pack.
 15. The method of claim 11, whereinreceiving periodic instances of battery pack data comprises utilizing atimer module configured to provide specific time intervals for the atleast a pack monitor unit to capture the battery pack data.
 16. Themethod of claim 11, wherein the battery pack model compriseselectrochemical model of a battery.
 17. The method of claim 11,predicting, by the computing device, the battery lifetime is furthercomprising utilizing a plurality of battery pack models.
 18. The methodof claim 11, wherein predicting a battery lifetime comprises training amachine-learning with training data derived from a battery database,wherein the machine-learning model is configured to output the batterylifetime prediction.
 19. The method of claim 11, wherein the batterypack model further comprises a plurality of virtual instances of thebattery pack data updated continuously.
 20. The method of claim 19,further comprising storing, by the computing device, the plurality ofvirtual instances of the battery pack data in a battery databasecommunicatively connected to a battery management system integrated intothe battery pack.